Terrain adaptive powered joint orthosis

Information

  • Patent Grant
  • 9687377
  • Patent Number
    9,687,377
  • Date Filed
    Monday, January 23, 2012
    12 years ago
  • Date Issued
    Tuesday, June 27, 2017
    7 years ago
Abstract
A powered device augments a joint function of a human during a gait cycle using a powered actuator that supplies an augmentation torque, an impedance, or both to a joint. A controller estimates terrain slope and modulates the augmentation torque and the impedance according to a phase of the gait cycle and the estimated terrain slope to provide at least a biomimetic response. The controller may also modulate a joint equilibrium. Accordingly, the device is capable of normalizing or augmenting human biomechanical function, responsive to a wearer's activity, regardless of speed and terrain, and can be used, for example, as a knee orthosis, prosthesis, or exoskeleton.
Description
FIELD OF THE INVENTION

This invention relates generally to powered human augmentation devices, such as lower-extremity prosthetic, orthotic, or exoskeleton apparatus, designed to emulate human biomechanics and to normalize function, components thereof, and methods for controlling the same.


BACKGROUND

Approximately 65% of service members seriously injured in Iraq and Afghanistan sustain injuries to their extremities. Many of these individuals experience muscle tissue loss and/or nerve injury, resulting in the loss of limb function or substantial reduction thereof. Many devices used for the treatment of lower-extremity pathology, e.g., knee orthoses, are passive devices. Increasingly, robotic technology is employed in the treatment of individuals suffering from limb pathology, either for the advancement of therapy tools or as permanent assistive devices. Upper-extremity robotic devices provide assistance and therapy for improved reaching and manipulation and, lower-extremity robotic devices have been developed for the enhancement of locomotor function.


Although decades of research has been conducted in the area of active permanent assistive devices for the treatment of lower-extremity pathology, these devices are not designed to produce a biomimetic response, generally described in terms of joint torque, joint angle, and other related parameters as observed in a human not having substantial muscle tissue injury and not using any device to assist in ambulation. Therefore, the robotic devices usually result in unnatural ambulation and may even cause significant discomfort to the wearer.


As such, many commercially available knee orthoses remain passive and non-adaptive to the wearer even today. These devices typically stabilize the knee joint medial-laterally, and limit the extent of knee flexion and extension. As such, they do not provide power or significant assistance to the user in walking, getting out of a chair, and ascending slopes and stairs, etc.


In level-ground walking, a healthy biological knee generally behaves like a spring during early to mid-stance, where knee torque is proportional to knee angular position. Further, during slope descent, the biological knee generally behaves like a variable damper, dissipating mechanical energy as heat to lower the body's center of mass with each step. Still further, during slope ascent, the biological knee behaves like a torque source, applying a non-conservative propulsive torque throughout early to mid-stance to lift the body's center of mass upwards with each step.


Some common major complications of knee extensor weakness are an inability to apply: 1) damping control during slope/stair descent, 2) spring stiffness control during early to mid-stance in level-ground walking, and 3) non-conservative propulsive torque control for slope/stair ascent and sit-to-stand maneuvers. Due to these various complications, a patient with knee extensor weakness frequently experiences a decrease in self-selected walking speed for level-ground and slope/stair ground surfaces, as well as an increase in walking metabolism while traversing these ground surfaces. Therefore, there is a need for improved systems and methods of permanent assistive devices for the treatment of lower-extremity pathology.


SUMMARY

In various embodiments, the present invention provides devices and methods for operating/controlling such devices so as to assist humans with knee extensor weakness, normalizing and/or enhancing the wearer's self-selected walking speed and metabolic economy. This is achieved using a type of device called Powered Knee Othosis (PKO); the PKO devices are capable of capable of spring stiffness control, dissipative damping control, and non-conservative torque control in both knee flexion and extension directions, in accordance with the gait-cycle, terrain (e.g., ground slope and stairs), and walking speed. As such, the PKO devices can adaptively provide a non-conservative propulsive torque to assist the user in walking, getting out of a chair, and ascending slopes and stairs.


The PKO devices can also augment knee torque during late stance, particularly during slope and/or stair ascent. Thus, the PKO devices can provide at least a biomimetic response and optionally can be used to enhance normal biomechanical response. Offering control enhancement for both stance and swing phases, a PKO device can be used as a permanent assistive device where actuation, sensing, power, and computation are all packaged within a small, lightweight, autonomous, manufacturable, and high cycle-life package that can readily fit within a normal pant leg, and can assist humans with weak or absent quadriceps. PKO devices can also assist humans having uninjured leg musculature in activities such as carrying a heavy load over a long distance and/or increasing elevation, to enhance their strength and endurance.


In one aspect, a method for assisting a person walking on a surface with a powered human augmentation device includes a controller. The method includes using the controller for determining a phase of a gait cycle, and estimating within the gait cycle, a slope of the surface. The method also includes supplying to a joint (e.g., knee) an augmentation torque, an impedance, or both. The impedance includes a linear spring component and a damping component. The method also includes modulating the augmentation torque and the impedance based on the phase of the gait cycle and the estimated slope, to provide at least a biomimetic response.


In some embodiments, the estimated slope is indicative of a walking mode such that level-ground walking mode corresponds to a substantially zero slope, downslope walking mode corresponds to a negative slope, and upslope walking mode corresponds to a positive slope. The downslope walking mode may include descending stairs and the upslope walking mode may include ascending stairs. The joint may be a knee.


In some embodiments, the method includes estimating walking speed, and the augmentation torque and/or the impedance may be based on the estimated walking speed. If the phase of the gait cycle is determined to be one of early stance and mid stance and the estimated slope is substantially zero, the impedance may be modulated such that contribution of the linear spring component to the modulated impedance is greater than contribution of the damping component. If the phase of the gait cycle is determined to be one of early stance and mid stance and the estimated slope is negative, however, the impedance is modulated such that contribution of the damping component is increased substantially compared to contribution thereof if slope is estimated to be substantially zero. Modulating the impedance may include varying the damping component according to the negative slope.


In some embodiments, the augmentation torque includes a non-conservative propulsive torque. If the phase of the gait cycle is determined to be one of early stance and mid stance and the estimated slope is positive, the non-conservative propulsive torque is provided such that the modulated augmentation torque is greater than the modulated augmentation torque applied if the slope is estimated to be substantially zero. If the phase of the gait cycle is determined to be late stance, the augmentation torque may be modulated to correspond to a reflex torque that is related to the estimated slope.


The method may include the step of modeling a joint equilibrium as a second-order response to a joint-position goal to be achieved prior to a next phase of the gait cycle. The modeling may be performed during a swing phase of the gait cycle. The method may also include determining if the joint is substantially fully flexed, during a swing phase of the gait cycle. If the joint is determined to be substantially fully flexed, modulating includes adjusting both the augmentation torque and the impedance to be substantially zero. In some embodiments, if the phase of the gait cycle is determined to be early swing, the augmentation torque is modulated according to the joint-equilibrium model such that a joint equilibrium corresponds to the joint-position goal. The impedance may be modulated according to the joint-equilibrium model such that a joint equilibrium corresponds to the joint-position goal.


In some embodiments, estimating the slope includes kinematically reconstructing a path of the joint (e.g., a knee) within the gait cycle. The method may also include kinematically reconstructing a path of another joint (e.g., an ankle) within the gait cycle, and associating the path of the other joint with the path of the joint to estimate the slope. The kinematic reconstruction may include computing a pose and an origin of a co-ordinate frame associated with a link connected to at least one of the joint and another joint proximal to the joint. The step of computing the pose may include creating a homogeneous transformation of the co-ordinate frame. In some embodiments, the homogeneous transformation includes a 3×1 vector defining an origin of the co-ordinate frame, and a 3×3 matrix comprising unit vectors of the co-ordinate frame. At least one point within the co-ordinate frame may correspond to a link connected to the joint and/or another joint proximal to the joint. The another joint may be an ankle joint and one point within the co-ordinate frame can be a distal end and/or a proximal end of a tibia connected to the ankle.


In some embodiments, the augmentation torque is modulated according to a positive-force feedback. The augmentation torque modulated according to the positive-force feedback, in combination with a natural joint torque supplied by the human, may approximate at least a normal joint torque. The positive-force feedback may include a gain and an exponent, and modulating may include adjusting the gain or the exponent, or both, according to the estimated slope and/or walking speed. The augmentation torque may be modulated according to a scaling factor and/or may be attenuated according to a protocol. The augmentation torque may be supplied in addition to natural joint torque supplied by the person to achieve at least a pre-determined total joint torque response.


In some embodiments, modulating includes applying a closed-loop torque control at the joint. To this end, the method may include modeling the joint torque, and determining the phase of the gait cycle based on the joint torque model. The augmentation torque, the impedance, and a joint equilibrium may be modulated in order to achieve at least a target walking speed, such as a walking speed desirable to the person. The augmentation torque, the impedance, and a joint equilibrium may also be modulated in order to substantially achieve a metabolic economy in accordance with a normative reference across at least one of walking speed and terrain.


In another aspect, embodiments of the invention feature a powered human augmentation device for assisting a person walking on a surface. The device includes a powered actuator for supplying to a joint an augmentation torque and/or an impedance that includes a linear spring component and a damping component. The device also includes a controller for (i) determining a phase of a gait cycle, (ii) estimating within the gait cycle a slope of the surface, and (iii) modulating the augmentation torque and the impedance based on the phase of the gait cycle and the estimated slope to provide at least a biomimetic response.


In some embodiments, the estimated slope is indicative of a walking mode, such that level-ground walking mode corresponds to a substantially zero slope, downslope walking mode corresponds to a negative slope, and upslope walking mode corresponds to a positive slope. The downslope walking mode may include descending stairs and the upslope walking mode may include ascending stairs. The joint may be a knee.


In some embodiments, the controller is adapted to estimate walking speed, and the augmentation torque, the impedance, or both may be based on the estimated walking speed. If the controller determines the phase of the gait cycle to be one of early stance and mid stance and the estimated slope is substantially zero, the powered actuator may be adapted to provide the modulated impedance such that contribution of the linear spring component to the modulated impedance is greater than contribution of the damping component. If the controller determines the phase of the gait cycle to be one of early stance and mid stance and the estimated slope is negative, the powered actuator may be adapted to provide the modulated impedance such that contribution of the damping component is increased substantially compared to contribution thereof if slope is estimated to be substantially zero. The controller may also be adapted to modulate the damping component according to the negative slope.


In some embodiments, the augmentation torque includes a non-conservative propulsive torque and, if the controller determines the phase of the gait cycle to be one of early stance and mid stance and the estimated slope is positive, the powered actuator may be adapted to provide the non-conservative propulsive torque such that the modulated augmentation torque is greater than the modulated augmentation torque applied if the slope is estimated to be substantially zero. If the controller determines the phase of the gait cycle to be late stance, the powered actuator may be adapted to provide the modulated augmentation torque, such that the modulated augmentation torque corresponds to a reflex torque that is related to the estimated slope.


In some embodiments, the controller is adapted to model, during a swing phase of the gait cycle, a joint equilibrium as a second-order response to a joint-position goal to be achieved prior to a next phase of the gait cycle. The device may include a joint angle sensor to provide a joint angle signal to the controller. If the controller determines, based on the joint angle signal, that the joint is substantially fully flexed, the powered actuator may adapted to adjust both the augmentation torque and the impedance to be substantially zero, during a swing phase of the gait cycle. If the controller determines the phase of the gait cycle to be early swing, the augmentation torque, impedance, or both may be modulated according to the joint-equilibrium model such that a joint equilibrium corresponds to the joint-position goal.


In some embodiments, the device includes an inertial measurement unit (IMU), and the controller may be adapted to kinematically reconstruct a path of the joint within the gait cycle based on a signal from the IMU. The controller may also be adapted to estimate the slope based on the kinematic reconstruction. The IMU may include an accelerometer and/or a gyroscope. The IMU may also include a first set of sensors associated with the joint (e.g., a knee) and a second set of sensors associated with another joint (e.g., an ankle). The controller may be adapted to kinematically reconstruct a path of the other joint within the gait cycle based on signals from the second set of sensors, and to associate the path of the other joint with the path of the joint to estimate the slope of the terrain.


The augmentation torque may be modulated according to a positive-force feedback. The augmentation torque modulated according to the positive-force feedback, in combination with a natural joint torque supplied by the human, may approximate at least a normal joint torque. The positive-force feedback may include a gain and an exponent, and modulating may include adjusting the gain, the exponent, or both according to the estimated slope and/or walking speed. The controller may be adapted to modulate the augmentation torque according to a scaling factor. In some embodiments, the device includes a communication interface for receiving a protocol, and the controller may be adapted to attenuate the augmentation torque according to the received protocol. The augmentation torque may be supplied in addition to natural joint torque supplied by the person to achieve at least a pre-determined total joint torque response.


In some embodiments, the controller is adapted to apply a closed-loop torque control at the joint. The controller may be adapted to model the joint torque, and to determine the phase of the gait cycle based on the joint torque model. The powered actuator may include a series-elastic actuator, and the series-elastic actuator may include a transverse-flux motor. In some embodiments, the series-elastic actuator includes a bilateral spring and a cable drive. The series-elastic actuator may also include a buckled beam and/or a unidirectional spring.


These and other objects, along with advantages and features of the embodiments of the present invention herein disclosed, will become more apparent through reference to the following description, the accompanying drawings, and the claims. Furthermore, it is to be understood that the features of the various embodiments described herein are not mutually exclusive and can exist in various combinations and permutations. As used herein, the term “substantially” means±10% and, in some embodiments, ±5%.





BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments of the present invention are described with reference to the following drawings, in which:



FIG. 1 illustrates biological knee function of an average human in the stance and swing phases of a human gait cycle during level-ground ambulation;



FIG. 2 illustrates how the knee response of an average human, described in terms of angle, moment (i.e., torque), and power, changes as a function of terrain slope;



FIG. 3a illustrates how the knee response may become impaired when the quadriceps extensors are weakened;



FIG. 3b illustrates how the knee response of FIG. 3a can be augmented, according to one embodiment;



FIGS. 4a and 4b schematically illustrate, during early stance and late stance, respectively, the terrain-based modulation of various components of knee extensor torque supplied by a powered augmentation device so as to normalize the knee response, according to one embodiment;



FIG. 4c shows adjustment of various torque and impedance parameters according to terrain and/or walking speed, according to one embodiment;



FIG. 5 schematically depicts a powered augmentation device according to one embodiment;



FIG. 6 illustrates the operation of a state machine of a powered augmentation device according to one embodiment;



FIG. 7 illustrates the operation of a powered augmentation device implementing the state machine of FIG. 6, according to one embodiment;



FIGS. 8a-8c schematically depict a powered augmentation device according to another embodiment;



FIGS. 8d and 8e illustrate closed-loop control of the powered augmentation device depicted in FIGS. 8a-8c, according to two embodiments, respectively;



FIG. 9 illustrates seamless integration of a powered augmentation device with a leg of a human, according to one embodiment;



FIG. 10 depicts kinematic reconstruction by a controller for controlling a powered augmentation device according to one embodiment; and



FIGS. 11a and 11b depict ankle and knee paths, respectively, each derived using measurements from an inertial measurement unit, according to one embodiment.





DESCRIPTION

The entire contents of each of U.S. patent application Ser. No. 12/157,727 “Powered Ankle-Foot Prosthesis” filed on Jun. 12, 2008 (Publication No. US2011/0257764 A1); U.S. patent application Ser. No. 12/552,013 “Hybrid Terrain-Adaptive Lower-Extremity Systems” filed on Sep. 1, 2009 (Publication No. US2010/0179668 A1); U.S. patent application Ser. No. 13/079,564 “Controlling Power in a Prosthesis or Orthosis Based on Predicted Walking Speed or Surrogate for Same” filed on Apr. 4, 2011; and U.S. patent application Ser. No. 13/079,571 “Controlling Torque in a Prosthesis or Orthosis Based on a Deflection of Series Elastic Element” filed on Apr. 4, 2011 are incorporated herein by reference.



FIG. 1 illustrates biological knee function in the stance and swing phases of a human gait cycle during level-ground ambulation. Throughout early stance 102 to mid stance 104 the knee 120 typically responds as a linear spring. This form of mechanical impedance (which can take the form of a spring, inertia or damper, acting alone or in combination) serves to cushion the foot-strike impact in accordance with the gait speed. In late-stance 106, the knee 120 generally behaves as a torque source in the form of a reflex to lift the lower leg 122 off the ground surface 130 during initiation portion 108 of the swing phase. The reflex release may arise from a positive force feedback mechanism within the gastrocnemius muscle. In the terminal portion 110 of the swing phase, the knee 120 first brakes the swinging lower leg 122 to limit heel rise after toe-off and then positions the lower leg 122 optimally for absorbing energy prior to foot strike initiation in the next gait cycle.


Typically, the human gait adapts to terrain modality, e.g., ground slope and whether the human is ascending or descending stairs, and to walking speed so as to maintain balance and to achieve a metabolically economical movement pattern. FIG. 2 illustrates how the knee response, described in terms of angle, moment (torque), and power, changes as a function of terrain slope. For example, during level-ground walking depicted by curve 202, the biological knee behaves like a spring, where knee torque is proportional to knee angular position, during early to mid-stance 212. During slope descent, depicted by curve 204, the biological knee behaves like a variable damper, dissipating mechanical energy as heat to lower the body's center of mass with each step, during early to mid-stance 212. The variable damping generally increases as the declination angle increases. Such behavior may also be invoked during stair descent. During slope ascent, depicted by curve 206, the biological knee behaves like a torque source, applying a non-conservative propulsive torque throughout early to mid-stance 212 to lift the body's center of mass upwards with each step. Such behavior is usually also invoked upon stair ascent. A slope-dependent reflex is applied in late stance 214.


Flexion angle in the swing phase also shows terrain dependence. In slope ascent, the flexion angle just prior to foot-strike, i.e., late swing 222 of the curve 232 increases with the slope of ascent, whereas the knee flexion is invariant with the slope of descent, as depicted by the curve 234. To achieve sufficient toe clearance on descent, the knee flexion angle increases in early swing 224 as the descent becomes steeper. Though the data presented in FIG. 2 are captured at a substantially constant gait speed, it is understood that the above impedance and torque response on level ground and slopes typically changes with gait speed, in part, to account for changes in the body momentum and to deliver/absorb power accordingly.


PKO platforms 500, 800 described with reference to FIGS. 5 and 8, respectively, can discriminate terrain modality and speed within a gait cycle (intra-cycle), and can also adapt the impedance, reflex, and position response in accordance with that terrain and gait speed. Intra-cycle sensing is advantageous, because during an average walk terrain and walking speed may change frequently. The platforms 500, 800 employ a six-degree-of-freedom inertial measurement unit (IMU) capable of computing the path of the ankle joint and the distal-end of the femur (knee), from which the IMU can discriminate and discern terrain modality, including stairs and slopes, as illustrated with reference to FIG. 11b. The path of the hip can be used to augment the information from the knee and ankle. For instance, in stair ascent, the hip is generally stationary as the knee flexes, a precursor that is not evident when a wearer is traversing sloping and/or level ground.



FIG. 3a illustrates how the knee response may become impaired when the quadriceps extensors are weakened. In early stance 302, the knee stiffness can be insufficient to absorb energy either as a spring as in level-ground ambulation or as a damper in steep descent. In late stance 304, the knee torque is insufficient to “brake” the knee and to deliver sufficient reflex particularly in steep ascent and descent.


When worn by a wearer with weakened quadriceps extensors, the PKO platforms 500, 800 deliver an augmentation torque, Γaugment, to normalize the response, i.e., to produce a response that may be produced by a joint (e.g., knee) of average humans not having weakened muscle tissue (e.g., quadriceps extensors) and not wearing any powered prosthetic/orthotic devices. With reference to FIG. 3b, just prior to foot-strike in early stance 312, the PKO platforms 500, 800 apply a computed knee flexion angle and set the impedance, for energy absorption, in accordance with terrain slope. The terrain slope can be inferred from the ankle and knee trajectories and with instantaneous gait speed inferred from the IMU-computed angular pitch rate of the femur and tibia.


Once the foot strikes the ground in early stance 312, the PKO platforms 500, 800 apply appropriate knee extensor torque, τextensor, to achieve an impedance relation of the form:

τextensor0(φ,{dot over (s)})−kφ,{dot over (s)}(θ−θ0)−bφ,{dot over (s)}{dot over (θ)}

in accordance with the computed terrain slope and speed. In late stance 314, the PKO platforms 500, 800 apply additional torque and reflex in accordance with the terrain slope and the instantaneous gait speed inferred by femur and tibia pitch rates. In late stance 314, the knee extensor torque corresponds to a biologically-conceived, non-linear, positive torque feedback relation of the form:







τ
extensor

=



P

ff

ϕ
,

s
.






(


Γ
knee


Γ
0


)



N

ϕ
,

s
.









where the gain, Pffφ,{dot over (s)} is a function of terrain slope, φ, and gait speed, and the exponent, Nφ,{dot over (s)}, is also a function of terrain slope and gait speed. Γknee is an intrinsic measure of knee torque in the above relation that includes the contribution of both the “locking torque” of the knee and the normalized extensor/flexor contribution. In general, both the gain and the exponent are increased to achieve the higher reflex torques needed as the slope of ascent and descent increase.


With reference to FIG. 4a, in early stance, during level-ground walking, the linear spring component k 402 of the extensor torque applied by the PKO platforms 500, 800 is significant. While descending slope, the linear spring component k 402 is decreased and the damping component b 404 is increased, such that the damping component b 404 is significant. While ascending slope, both the linear spring k 402 and damping component b 404 are decreased and a non-conservative propulsive torque component Γ0 406 is increased.


With reference to FIG. 4b, in late stance, during level-ground walking, the knee extensor torque applied by the PKO platforms 500, 800 corresponds to non-linear, positive torque feedback determined by gain 412 and exponent 414. While descending slope, the gain 412 is decreased and the exponent 414 is increased. While ascending slope, both the gain 412 and exponent 414 are increased. Adjustment of various torque and impedance parameters according to terrain and/or walking speed is described in a Table in FIG. 4c. Thus, the PKO platforms 500, 800 can emulate human knee behavior during the gait cycle by biomimetically applying impedance, torque, and joint equilibrium control in accordance with the gait cycle and speed, and augment the knee torque of the wearer to provide at least a normalized knee response.


With reference to FIG. 5, the PKO platform 500 uses a quiet, light-weight, and rugged actuator 502. A modular battery 504 having a 3000 step capacity (typically for a wearer weighing about 70 kg with significant quadriceps extensor weakness) is used. A typical wearer may need to replace this lightweight battery pack 504 between one and two times per day. The actuator 502 can deliver at least biomimetic torque and angle response within a gait speed range from about 0 up to about 1.75 m/sec.


Optionally, the Platform 500 may employ one or two embedded wireless interfaces 506. A Bluetooth® interface may be used as the pathway for PDA-based tuning by clinicians and researchers to normalize the torque response, e.g., by specifically programming the PKO platform 500 to deliver augmentation torque Γaugment as required in each phase of the gait cycle as described below with reference to FIG. 7. A smart WiFi interface may serve as the pathway for researchers to acquire control state variables and sensory feedback from the PKO platform 500 and to synchronize this telemetry with external biomechanical instrumentation.


The actuator 502 of the PKO platform 500 can be a series-elastic actuator (SEA) to drive the powered orthosis. See, for example, U.S. Pat. No. 5,650,704 “Elastic Actuator for Precise Force Control” the disclosure of which is incorporated herein by reference. A multi-processor control system (State and Actuator Controller) 508 uses feedback from the SEA to deliver the appropriate response in accordance with the phase of the gait cycle, the terrain, and the walking speed. A three-phase brushless motor driver (Motor Driver) 522 interfaces to the State and Actuator Controller 508 to accomplish closed-loop torque control of the SEA 502. An Inertial Measurement Unit (IMU) 510, employing a three-axis rate gyro and a three-axis accelerometer, provides feedback to sense transitions between phases of the gait cycle, to measure gait speed, and to discriminate terrain modality. The WiFi/Bluetooth® communication module 506 is employed to interface directly to the State Controller and Actuator Controller 508 to facilitate data acquisition and PDA-based clinician tuning.


The SEA 502 may employ a robust ball-screw mechanism 524 driven by the high-rpm brushless motor 522 through a redundant aramid fiber twin belt transmission 526. The ball-nut 524 of the SEA 502 drives the knee 540 through a bilateral spring assembly 528 and a redundant aramid fiber cable drive 530. The bilateral spring assembly 528 can exhibit a weak stiffness in flexion and a stiffer spring in extension as would be applied in locking the knee joint. Thus in this embodiment, the bilateral spring 528 is used (i) to store energy in late stance for later release in the reflex response and (ii) to serve as a sensing means for achieving closed-loop torque control of the actuator 502. By storing energy for later release, the peak power and, hence, size and weight of the motor 522 are reduced by over 40% compared to an actuator without the spring storage, in this embodiment. Displacement of the spring 528 can be used to estimate and thereby control drive torque in a way that attenuates the effect of friction, enabling a backdrivable means of actuation that replicates biological knee operation.


A knee sensor 532, a motor-position sensor 534, and a ball-screw position sensor 536 embedded in the actuator 502 are employed to determine a state of the actuator 502 and to provide a basis for brushless motor control and for modulation of impedance, torque, and position in accordance with the phase of the gait cycle and gait speed. To this end, the State Controller and Actuator Controller 508 implements a state machine.


With reference to FIG. 6, during early stance state 602, the state machine 600 adapts the PKO platform 500 to apply a linear spring and damping impedance in accordance with the gait speed and terrain angle, given by:

τextensor0(φ,{dot over (s)})−kφ,{dot over (s)}(θ−θ0)−bφ,{dot over (s)}{dot over (θ)}
kcp=kcp(φ,{dot over (s)});bcp=bcp(φ,{dot over (s)})

Where


τextensor is the commanded SEA motor torque


θ is the ankle angle,


φ is the terrain angle, and


{dot over (s)} is the estimated gait speed at foot-strike estimated by the IMU


Transition into the early stance state 602 is accomplished by sensing by the IMU 510 the distinctive vibration that occurs when the foot strikes the ground. The impedance is configured and scaled so as to prevent buckling of the knee in accordance with walking speed and the response needed to at least normalize the augmented response of the wearer.


Transition into the late stance state 604 generally occurs when the detected knee extension angle velocity changes from negative to positive. In this state 604, a reflex response can be achieved through non-linear positive feedback as described by the relation:







τ
extensor

=



P

ff

ϕ
,

s
.






(


Γ
knee


Γ
0


)



N

ϕ
,

s
.









In this, the reflex gain, Pff(φ,{dot over (s)}) and the exponent (non-linear spring), N(φ,{dot over (s)}) are each a function of the terrain angle, φ, and the estimated gait speed, {dot over (s)}={dot over (s)}({dot over (ψ)}femur, {dot over (ψ)}tibia), which is a function of the instantaneous angular rate of the tibia and femur at the time of entry in to the late stance state 604. A hard stop spring model for extreme knee extension, Γknee(θ), is used to model the wearer torque response at extremes of extension (θ>0) while the knee is locked so that at least a biomimetic response is achieved.


Transition into early swing state 606 occurs when the detected SEA 502 torque, ΓSEA, approaches a programmable percentage of peak torque. In this state 606, position control is employed to brake the knee flexion velocity, to achieve proper ground clearance and heel rise during the early to mid swing phase through use of an organically-derived trajectory, θ0(t) that smoothly decelerates to a goal position in a nearly ballistic trajectory (i.e., small torque corresponding to a lightly damped pendulum), θgoalgoal0goal0({circumflex over (φ)}|ls, {dot over (s)}):

τextensor=−kesw(θ−θ0)−besw({dot over (β)}motor−{dot over (β)}motor0)
τtrajectory2{umlaut over (θ)}0+2τtrajectory{dot over (θ)}00goal0
θgoal0goal0({circumflex over (φ)}|ls,{dot over (s)});{dot over (β)}=J−1(θ){dot over (θ)}

where βmotor is the motor angle corresponding to a knee angle with zero SEA spring displacement. and


{circumflex over (φ)}|ls is estimated terrain angle as estimated at the end of late stance using the inertial tibia and femur angular velocities.


Also in the early swing state 606, the inertial ankle and knee trajectories are computed and used to discriminate between the three modalities, i.e., slope/stair ascent, slope/stair descent, and walking on substantially level ground. This early discrimination may be used to adjust the control parameters of the State Controller and Actuator Controller 508 in advance of foot strike to achieve seamless response across the swing-stance transition.


Transition into late swing state 608 occurs when the IMU 510 detects a negative, vertical Cartesian (world-frame referenced) ankle pivot velocity, WVankle pivotz. In this state 608, position control is used with a smooth trajectory that converges to a time-varying goal point, θgoal, that is a function of gait speed and terrain slope, each estimated by the IMU 510 which in some embodiments uses only intra-gait-cycle information. The impedance (stiffness and damping) applied to position and velocity errors referenced to the trajectory (equilibrium), θ0(t) may be preferably set in accordance with gait speed and terrain angle.



FIG. 7 illustrates how the PKO platform 500 can augment the torque of a wearer to achieve at least a normalized biomimetic response. In some embodiments, a powered augmentation device can augment the torque and adjust impedance to achieve a response that can enable a wearer who does not have a diminished natural joint function to perform activities such as walking or running a long distance, carrying a heavy load, climbing steep slopes, etc. The state machine 600 modulates the SEA 502 impedance, reflex, and position control response in accordance with gait speed and terrain modality inputs from the IMU 510. The SEA 502 control internally computes at least the normalized biomimetic torque, Γ*, in each state of the gait cycle. State-specific attenuation, set by the clinician, then scales Γ* and drives the SEA 502 to deliver just the right torque, Γaugment, to add to the wearer's natural torque response, Γwearer, to approximate Γ*, i.e., the desired normalized biomimetic response or an enhanced response that may allow a person to undertake activities such as walking fast (e.g., 2 m/sec.) for a long time e.g., about 6 hours.


Battery conservation is important in wearable PKO devices. In the absence of battery energy, or when the walking state machine (e.g., the state machine 600, illustrated with reference to FIG. 6) detects that the wearer has stopped walking (which can be determined by absence of gait-cycle phase transition for over approximately two seconds), the control system shorts the motor leads to ground using power electronics. In this special damping mode the motor leads are shorted together, creating a dynamic brake with damping torque,








τ
motor

=



-

b
sl



ω

=


-



(


k
g



k
t


)

2

R



ω



,





where bsl is the shorted leads damping, kg is the gear ratio between the motor and joint output, kt is the motor constant in Nm/A and R is the motor resistance, and ω is the rotation rate of the joint. In the “shorted leads” operation, the time constant, τsl, that describes the first-order spring-damper actuator dynamics comprising the series-spring, kSEA and the intrinsic actuator damping, bsl, is given by the relation,








τ
sl

=


b
sl


k
SEA



,





In transverse-flux and other high-torque motor actuators, the τsl may be on the order of about 500 msec or more. For time intervals, e.g., less than ⅓ of the time constant, the actuator 502 in “shorted leads” mimics a static clutch, taking no energy from the battery. By matching the series-stiffness with that required in early stance flexion, the motor clutch is engaged at the desired joint equilibrium so as to approximate the biomimetic linear spring response without requiring any battery energy. This affords significant advantage in system design, response, and economy of operation.



FIGS. 8a-8c depict a PKO device 800 that employs a buckled beam 812 as the series-elastic element of the SEA 802. The SEA 802 includes a high RPM brushless, permanent magnet motor 814 having an integral heat sink and an insulator. The motor 814 can be a radial motor, a transverse-flux motor, a stepping motor, etc. The SEA 802 also includes a sealed ball-screw mechanism 816 having a 14 mm diameter and 3 mm lead, in this embodiment. It should be understood that these dimensions are illustrative only and are not limiting.


The motor 814 is coupled to the buckled beam via a flexural coupling 818 to protect the ball-screw mechanism 816 from moment load, a reverse-cam linkage 820, and sealed needle bearings 824. The needle bearings 824 typically have L1 design life of over five million cycles (i.e., a design whereby 99% of a population survive longer than the reported design life with 95% statistical confidence). The PKO 800 also includes an integral pivot scaffold SEA support 826, coupled to the motor 814, and a foot support 828 (e.g., a custom nylon foot support), coupled to the buckled beam 812. The reverse-cam linkage 820 includes an encoder 830 that may be used to determine the SEA torque based on a torque-angular displacement model. The encoder 830 can be a 13-bit absolute encoder having a torque resolution of about 8 bits.


In one embodiment, the motor 814 is controlled in a closed loop. FIG. 8d illustrates one embodiment of an implementation of the closed-loop torque control in the PKO 800, in which the Joint Torque Command Generator 852 computes the commanded joint torque, Γjoint, from terrain, φ, walking speed, {dot over (s)}, and gait-cycle phase as these are supplied from a State Controller (e.g., State and Actuator Controller 508, described with reference to FIG. 5). The Joint-Torque Model 854 estimates the actual applied joint torque, Γjoint, from wearer knee extension, wearer extensor-flexor and buckling-beam 812 (for series-elasticity) torque contributions. The wearer contributions may be assumed to be a percentage of a normative amount or a percentage of the command torque. The contribution of the buckling-beam 812 (series elastic component of the SEA 802, in general) may be estimated from off-wearer calibration during testing of the PKO device 800.


The difference in the commanded and applied torque, δΓjoint, is scaled by the nominal stiffness of the buckling beam 812 (generally, the SEA) and is passed through a proportional-integral-derivative (PID) compensator 856, G1 (z−1), to compute a commanded value of deflection, β−θ, where θ is the joint angle and β is the joint angle specified by the actuator for approximately zero buckled beam (SEA) deflection. G1 is designed with at least integral compensation with saturation error limits to force substantially zero steady-state torque error and may typically include proportional and derivative terms. The sensed joint angle, θsense, is added by an adder 858 to the deflection command to compute a commanded actuator angle, βcommanded.


The estimated actuator displacement is derived by actuator kinematics 860 by sensing the motor angle, p, which is used in a computational model, β(p), of the actuator kinematics 860. The actuator error is supplied to a second PID compensator 862 with actuator range of motion limits to deliver a motor torque, τmotor, to drive the actuator 802. A brushless, permanent magnet motor, either radial, transverse flux, or stepping motor, is commutated electronically using a multiphase motor driver that delivers a torque-producing current component, iq, to achieve the desired motor torque via the relation τmotor=ktiq, where kt is the motor torque constant in Nm/A. If a stepping motor is used, the motor can be stepped in a closed-loop fashion to align with the position command.


In another embodiment illustrated with reference to FIG. 8e, the Joint Torque Model 854 supplies and estimated joint torque to the Joint Torque Command generator 852, which determines the augmentation torque command, Γjoint. The torque command is passed through a command shaping filter 864, having a transfer function G1 (z−1) and a torque de-scaling,







1

k
SEA


,





to create a high-fidelity deflection signal. The command shaping filter 864 may be a low-pass filter to ensure that the inner deflection control loop has sufficient response bandwidth to follow the command. Other embodiments may be implemented by those skilled in the art to deliver a joint torque response that closely matches the desired biomechanical response as this is achieved through modulation of impedance, joint equilibrium, and torque in accordance with gait-cycle phase, terrain and walking speed.


Seamless integration of the PKO platform 500 onto a wearer is desirable to ensure that the torque supplied by the PKO platform 500 is coupled efficiently to the joint (knee, ankle, etc.). With reference to FIG. 9, in some embodiments, a process is provided for custom manufacturing an upper cuff assembly 902 and a lower cuff assembly 904 to conform/couple directly to the wearer. For each wearer a three-dimensional scanning tool is employed to measure those body surfaces that must integrate with the PKO platform 500. From these surface measurements, lightweight titanium forms can be printed (e.g., using a direct-write process). These can be functionalized through heat treating to create the scaffold upon which a custom 3-D printed elastomer, with spatially-varying durometer, can be bonded to achieve the desired custom integration.


In some embodiments, the State and Actuator Controller 508 is adapted to kinematically reconstruct a joint path. Such reconstruction can be used to determine the terrain (e.g., whether the terrain is level ground, sloping ground, or stairs), and activity (i.e., whether the wearer is walking on level ground, upslope, or downslope, or walking up or down the stairs). The modulation of the toque, impedance, and joint equilibrium may be based on the terrain and activity as determined via the kinematic reconstruction.



FIG. 10 illustrates a method for determining, via kinematic reconstruction, ankle joint 1000, heel 1012 and toe 1016 paths while using any PKO device (e.g., the PKO platforms 500, 800) based on the inertial pose of a lower leg member 1020 coupled to the ankle joint 1000, and the angle between the lower leg member 1020 and foot member 1008. Pose is the position and orientation of a coordinate system. The IMU (e.g., the IMU 510) may be coupled to the lower leg member 1020. The IMU may include a three-axis rate gyro for measuring angular rate and a three-axis accelerometer for measuring acceleration. Placing the inertial measurement unit on the lower leg member 1020 collocates the measurement of angular rate and acceleration for all three axes of the lower leg member 1020. The inertial measurement unit provides a six-degree-of-freedom estimate of the lower leg member 1020 pose, inertial (world frame referenced) orientation and ankle-joint 1000 (center of rotation of the ankle-foot) location.


In some embodiments, the lower leg member 1020 pose is used to compute the instantaneous location of the knee joint. By using knowledge of the ankle joint 1000 angle (θ) the instantaneous pose of the bottom of the foot 1008 can be computed, including location of the heel 1012 and toe 1016. This information in turn can be used when the foot member 1008 is flat to measure the terrain angle in the plane defined by the rotational axis of the ankle joint/foot member. Mounting the inertial measurement unit on the lower leg member 1020 has advantages over other potential locations. Unlike if it were mounted on the foot member 1008, the lower leg member 1020 mounting protects against physical abuse and keeps it away from water exposure. Further, it eliminates the cable tether that would otherwise be needed if it were on the foot member 1008—thereby ensuring mechanical and electrical integrity. Finally, the lower leg member 1020 is centrally located within the kinematic chain of a hybrid system facilitating the computation of the thigh and torso pose with a minimum of additional sensors.


The inertial measurement unit can be used to calculate the orientation, anklewO, position, anklewp, and velocity, anklewv, of the PKO platform in a ground-referenced world frame. anklewO may be represented by a quaternion or by a 3×3 matrix of unit vectors that define the orientation of the x, y and z axes of the ankle joint in relation to the world frame. The ankle joint 1000 coordinate frame is defined to be positioned at the center of the ankle joint axis of rotation with its orientation tied to the lower leg member 1020. From this central point, the position, velocity and acceleration can be computed. For points of interest in, for example, the foot (e.g., the heel 1012 or toe 1016), a foot member-to-ankle joint orientation transformation, footankleO(θ) is used to derive the position using the following relation:

point-of-interestwp=anklewp+anklewO(γ)footankleO(θ)(footrpoint-of-interest)

where










foot
ankle


O



(
γ
)


=

[



1


0


0




0



cos


(
γ
)





-

sin


(
γ
)







0



sin


(
γ
)





cos


(
γ
)





]






where γ is the inertial lower leg member angle, and










foot
ankle


O



(
θ
)


=

[



1


0


0




0



cos


(
θ
)





-

sin


(
θ
)







0



sin


(
θ
)





cos


(
θ
)





]






where θ is the ankle joint angle.


In this embodiment, the inertial measurement unit, including the three-axis accelerometer and three-axis rate gyro, is located on the forward face at the top of the lower leg member 1020. It is advantageous to remove the effect of scale, drift and cross-coupling on the world-frame orientation, velocity and position estimates introduced by numerical integrations of the accelerometer and rate gyro signals


Inertial navigation systems typically employ a zero-velocity update (ZVUP) periodically by averaging over an extended period of time, usually seconds to minutes. This placement of the inertial measurement unit is almost never stationary in the lower-extremity devices such as a PKO. However, the bottom of the foot is the only stationary location, and then only during the controlled dorsiflexion state of the gait cycle. An exemplary zero-velocity update method, which is not impacted by this limitation, for use with various embodiments of the invention is described further below.


To solve this problem, orientation, velocity and position integration of ankle joint is performed. After digitizing the inertial measurement unit acceleration, IMUa, the ankle joint acceleration (IMUaankle) is derived with the following rigid body dynamic equation:

IMUaankle=IMUa+IMU{right arrow over (ω)}XIMU{right arrow over (ω)}XankleIMU{right arrow over (r)}+{dot over ({right arrow over (ω)})}XankleIMU{right arrow over (r)}

where IMU{right arrow over (ω)} and IMU{right arrow over ({dot over (ω)})} are the vectors of angular rate and angular acceleration, respectively, in the inertial measurement unit frame and X denotes the cross-product.


The relationship is solved anklewO=IMUwO similarly as in the equations above using standard strapdown inertial measurement unit integration methods, in accordance with the following relationships known to one skilled in the art:









ankle
w



Φ
^


=






w



Ω
^




(



w



ω
^


)


ankle
w



Φ
^










v
^

ankle



w

=



a
^

ankle



w

-


[

0
,
0
,
g

]

T










p
^

ankle



w

=


v
^

ankle



w










foot
w



Φ
^


=





ankle
w



Φ
^






foot
ankle



Φ
^



=




ankle
w



Φ
^





Rotation
x



(

Θ
^

)












v
^

heel



w

=



v
^

ankle



w

+





w



Ω
^




(





ankle
w



Φ
^




[



Θ
.

^






0





0

]


T

)




r

heel


-


ankle




w











v
^

toe



w

=



v
^

ankle



w

+





w



Ω
^




(





ankle
w



Φ
^




[



Θ
.

^






0





0

]


T

)




r

toe


-


ankle




w











p
^

heel



w

=



p
^

ankle



w

+

r

heel


-


ankle




w










p
^

toe



w

=



p
^

ankle



w

+

r

toe


-


ankle




w









r

heel


-


ankle




w

=




foot
w



Φ
^






foot



(


r
heel

-

r
ankle


)










r

toe


-


ankle




w

=




foot
w



Φ
^






foot



(


r
toe

-

r
ankle


)







In the equations above, the matrix, {circumflex over (Φ)}, will be used interchangeably with the orientation matrix, IMUwO. The world frame-referenced ankle joint velocity and position are then derived at a point in time after the time of the previous zero-velocity update (i-th zero-velocity update) based on the following:

wvankle(t)=∫ZVUP(i)t(IMUwO)IMUaankledt
wpankle(t)=∫ZVUP(i)t wvankledt

where wpankle(t=ZVUP(i)) is reset to zero for all i.


The six-degree-of-freedom inertial measurement unit (IMU) 510 of the PKO platform 500 or the IMU of the PKO device 800 is capable of computing the path of the ankle joint and the distal-end of the femur (knee) from which the IMU can discriminate and discern terrain modality—including stairs and slopes. With reference to FIG. 11a, inertially referenced ankle joint paths Wpankle joint(t), and ankle-velocity-attack-angle 1104, WVankle joint, on stairs and sloping ground can be used to discriminate stair ascent/descent from ascent/descent on sloping ground. The slope, φ, can be estimated as {circumflex over (φ)} in swing using the relation:

{circumflex over (φ)}=tan −1(Wpankle jointz(t),Wpankle jointy)


With reference to FIG. 11b, inertially-referenced knee path shape can be used to detect stair ascent/descent shortly after toe-off—enabling knee impedance and torque response to be configured prior to foot-strike on the stair. The “kink” 1110 in the knee path may signal impending foot strike on sloping ground, enabling a prediction of terrain slope using the ankle joint slope prediction described above with reference to FIG. 11a. Using the joint slope, speed and ankle velocity angle-of-attack, the joint equilibrium and impedance can be adjusted in preparation for the foot strike.


While the invention has been particularly shown and described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is thus indicated by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced.

Claims
  • 1. A powered human augmentation device for assisting a person walking on a surface, the device comprising: a powered actuator to supply to a joint at least one of an augmentation torque and an impedance comprising a linear spring component and a damping component;an inertial measurement unit (IMU) to generate a kinematic signal, the kinematic signal to include indications of positions of the joint during a gait cycle; anda controller to: determine whether a phase of the gait cycle is early- or mid-stance;receive the kinematic signal;reconstruct a path of the joint within the gait cycle based on the kinematic signal;estimate within the gait cycle a slope of a surface based on the reconstructed path;determine whether the estimated slope is substantially zero or negative; andmodulate at least one of the augmentation torque and the impedance based on the phase of the gait cycle and the estimated slope of the surface to provide at least a biomimetic response, wherein the modulating comprises at least one of: sending a control signal to the powered actuator based on a determination that the phase of the gait cycle is one of early stance or mid stance and a determination that the estimated slope of the surface is substantially zero, the control signal to cause the powered actuator to provide the modulated impedance such that contribution of the linear spring component of the modulated impedance is greater than contribution of the damping component of the modulated impedance, orsending a control signal to the powered actuator based on a determination that the phase of the gait cycle is one of early stance or mid stance and a determination that the estimated slope of the surface is negative, the control signal to cause the power actuator to provide the modulated impedance such that contribution of the damping component is substantially greater compared to the contribution of the damping component based on a determination that the estimated slope of the surface is substantially zero.
  • 2. The powered human augmentation device of claim 1, wherein the estimated slope is indicative of a walking mode such that level-ground walking mode corresponds to a substantially zero slope, downslope walking mode corresponds to a negative slope, and upslope walking mode corresponds to a positive slope.
  • 3. The powered human augmentation device of claim 2, wherein the downslope walking mode comprises descending stairs and the upslope walking mode comprises ascending stairs.
  • 4. The powered human augmentation device of claim 1, wherein the joint is a knee.
  • 5. The powered human augmentation device of claim 1, wherein the controller is adapted to estimate walking speed, and at least one of the augmentation torque and the impedance is based on the estimated walking speed.
  • 6. The powered human augmentation device of claim 1, wherein the augmentation torque comprises a non-conservative propulsive torque, the controller to: determine whether the phase of the gait cycle is one of early stance or mid stance;determine whether the estimated slope of the surface is positive; andsend a control signal to the powered actuator based on a determination that the phase of the gait cycle is one of early stance or mid stance and a determination that the estimated slope of the surface is positive, the control signal to cause the powered actuator to provide-the nonconservative propulsive torque such that the modulated augmentation torque is greater than the modulated augmentation torque applied based on a determination that the estimated slope of the surface is substantially zero.
  • 7. The powered human augmentation device of claim 1, the controller to: determine whether the phase of the gait cycle is late stance; andsend a control signal to the powered actuator based on a determination that the phase of the gate cycle is late stance, the control signal to cause the powered actuator to provide the modulated augmentation torque such that the modulated augmentation torque corresponds to a reflex torque that is related to the estimated slope of the surface.
  • 8. The powered human augmentation device of claim 1, wherein the controller is adapted to model, during a swing phase of the gait cycle, a joint equilibrium as a second-order response to a joint-position goal to be achieved prior to a next phase of the gait cycle.
  • 9. The powered human augmentation device of claim 8 further comprising a joint angle sensor to provide a joint angle signal to the controller, wherein if the controller determines, based on the joint angle signal, that the joint is substantially fully flexed, the powered actuator is adapted to adjust both the augmentation torque and the impedance to be substantially zero, during a swing phase of the gait cycle.
  • 10. The powered human augmentation device of claim 8, wherein if the controller determines the phase of the gait cycle to be early swing, the augmentation torque is modulated according to the modeled joint-equilibrium such that a joint equilibrium corresponds to the joint-position goal.
  • 11. The powered human augmentation device of claim 8, wherein if the controller determines the phase of the gait cycle to be early swing, the impedance is modulated according to the modeled joint-equilibrium such that a joint equilibrium corresponds to the joint-position goal.
  • 12. The powered human augmentation device of claim 1, wherein the IMU comprises at least one of an accelerometer and a gyroscope.
  • 13. The powered human augmentation device of claim 1, wherein the IMU comprises a first set of sensors associated with the joint and a second set of sensors associated with another joint, and the controller is adapted to: kinematically reconstruct a path of the other joint within the gait cycle based on signals from the second set of sensors; andassociate the path of the other joint with the path of the joint to estimate the slope.
  • 14. The powered human augmentation device of claim 1, wherein the augmentation torque is modulated according to a positive-force feedback.
  • 15. The powered human augmentation device of claim 14, wherein the augmentation torque modulated according to the positive-force feedback, in combination with a natural joint torque supplied by the human, approximates at least a normal joint torque.
  • 16. The powered human augmentation device of claim 14, wherein the positive-force feedback comprises a gain and an exponent.
  • 17. The powered human augmentation device of claim 16, wherein modulating comprises adjusting at least one of the gain and the exponent according to at least one of the estimated slope and walking speed.
  • 18. The powered human augmentation device of claim 1, wherein the controller is adapted to modulate the augmentation torque according to a scaling factor.
  • 19. The powered human augmentation device of claim 1 further comprising a communication interface for receiving a protocol, and the controller is adapted to attenuate the augmentation torque according to the received protocol.
  • 20. The powered human augmentation device of claim 1, wherein the augmentation torque is supplied in addition to natural joint torque supplied by the person to achieve at least a pre-determined total joint torque response.
  • 21. The powered human augmentation device of claim 1, wherein the controller is adapted to apply a closed-loop torque control at the joint.
  • 22. The powered human augmentation device of claim 21, wherein the controller is adapted to: model the toque at the joint; anddetermine the phase of the gait cycle based on the modeled joint torque.
  • 23. The powered human augmentation device of claim 1, wherein the powered actuator comprises a series-elastic actuator.
  • 24. The powered human augmentation device of claim 23, wherein the series-elastic actuator comprises a transverse-flux motor.
  • 25. The powered human augmentation device of claim 23, wherein the series-elastic actuator comprises a bilateral spring and a cable drive.
  • 26. The powered human augmentation device of claim 23, wherein the series-elastic actuator comprises a unidirectional spring.
  • 27. The powered human augmentation device of claim 23, wherein the series-elastic actuator comprises a buckled beam.
  • 28. A powered human augmentation device for assisting a person walking on a surface, the device comprising: a powered actuator to supply to a joint at least one of an augmentation torque and an impedance comprising a linear spring component and a damping component;an inertial measurement unit (IMU) to generate a kinematic signal, the kinematic signal to include indications of positions of the joint during a gait cycle; anda controller to: determine whether a phase of the gait cycle is early- or mid-stance;receive the kinematic signal;reconstruct a path of the joint within the gait cycle based on the kinematic signal;estimate within the gait cycle a slope of a surface based on the reconstructed path;determine whether the estimated slope is substantially zero or negative;and modulate at least one of the augmentation torque and the impedance based on the phase of the gait cycle and the estimated slope of the surface to provide at least a biomimetic response, wherein the modulating comprises sending a control signal to the powered actuator based on a determination that the phase of the gait cycle is one of early stance or mid stance and a determination that the estimated slope of the surface is substantially zero, the control signal to cause the powered actuator to provide the modulated impedance such that contribution of the linear spring component of the modulated impedance is greater than contribution of the damping component of the modulated impedance.
  • 29. A powered human augmentation device for assisting a person walking on a surface, the device comprising: a powered actuator to supply to a joint at least one of an augmentation torque and an impedance comprising a linear spring component and a damping component;an inertial measurement unit (IMU) to generate a kinematic signal, the kinematic signal to include indications of positions of the joint during a gait cycle; anda controller to: determine whether a phase of the gait cycle is early- or mid-stance;receive the kinematic signal;reconstruct a path of the joint within the gait cycle based on the kinematic signal;estimate within the gait cycle a slope of a surface based on the reconstructed path;determine whether the estimated slope is substantially zero or negative; andmodulate at least one of the augmentation torque and the impedance based on the phase of the gait cycle and the estimated slope of the surface to provide at least a biomimetic response, wherein the modulating comprises sending a control signal to the powered actuator based on a determination that the phase of the gait cycle is one of early stance or mid stance and a determination that the estimated slope of the surface is negative, the control signal to cause the power actuator to provide the modulated impedance such that contribution of the damping component is substantially greater compared to the contribution of the damping component based on a determination that the estimated slope of the surface is substantially zero.
RELATED APPLICATIONS

This application claims priority to and benefit of U.S. Provisional Patent Application Ser. No. 61/435,045, filed on Jan. 21, 2011, the entire content of which is hereby incorporated by reference in its entirety.

US Referenced Citations (331)
Number Name Date Kind
2489291 Henschke at al. Nov 1949 A
2529968 Sartin Nov 1950 A
3098645 Owens Jul 1963 A
3207497 Schoonover Sep 1965 A
3844279 Konvalin Oct 1974 A
4442390 Davis Apr 1984 A
4463291 Usry Jul 1984 A
4518307 Bloch May 1985 A
4532462 Washbourn et al. Jul 1985 A
4546295 Wickham et al. Oct 1985 A
4546296 Washbourn et al. Oct 1985 A
4546297 Washbourn et al. Oct 1985 A
4546298 Wickham et al. Oct 1985 A
4569352 Petrofsky et al. Feb 1986 A
4600357 Coules Jul 1986 A
4657470 Clarke et al. Apr 1987 A
4843921 Kremer Jul 1989 A
4865376 Leaver et al. Sep 1989 A
4872803 Asakawa Oct 1989 A
4909535 Clark et al. Mar 1990 A
4921293 Ruoff et al. May 1990 A
4921393 Andeen et al. May 1990 A
4923474 Klasson et al. May 1990 A
4923475 Gosthnian et al. May 1990 A
4964402 Grim et al. Oct 1990 A
4989161 Oaki Jan 1991 A
5012591 Asakawa May 1991 A
5049797 Phillips Sep 1991 A
5062673 Mimura Nov 1991 A
5088478 Grim Feb 1992 A
5092902 Adams et al. Mar 1992 A
5112296 Beard et al. May 1992 A
5174168 Takagi et al. Dec 1992 A
5181933 Phillips Jan 1993 A
5252102 Singer et al. Oct 1993 A
5294873 Seraji Mar 1994 A
5311109 Ozawa May 1994 A
RE34661 Grim Jul 1994 E
5327790 Levin et al. Jul 1994 A
5367790 Gamow et al. Nov 1994 A
5383939 James Jan 1995 A
5405409 Knoth Apr 1995 A
5442270 Tetsuaki Aug 1995 A
5443521 Knoth et al. Aug 1995 A
5456341 Garnjost et al. Oct 1995 A
5458143 Herr Oct 1995 A
5476441 Durfee et al. Dec 1995 A
5502363 Tasch et al. Mar 1996 A
5514185 Phillips May 1996 A
5556422 Powell, III et al. Sep 1996 A
5571205 James Nov 1996 A
5643332 Stein Jul 1997 A
5650704 Pratt et al. Jul 1997 A
5662693 Johnson et al. Sep 1997 A
5701686 Herr et al. Dec 1997 A
5718925 Kristinsson et al. Feb 1998 A
5748845 Labun et al. May 1998 A
5776205 Phillips Jul 1998 A
5885809 Effenberger et al. Mar 1999 A
5888212 Petrofsky et al. Mar 1999 A
5898948 Kelly et al. May 1999 A
5910720 Williamson et al. Jun 1999 A
5932230 DeGrate Aug 1999 A
5971729 Kristinsson et al. Oct 1999 A
5972036 Kristinsson et al. Oct 1999 A
5980435 Joutras et al. Nov 1999 A
6029374 Herr et al. Feb 2000 A
6056712 Grim May 2000 A
6067892 Erickson May 2000 A
6071313 Phillips Jun 2000 A
6136039 Kristinsson et al. Oct 2000 A
6144385 Girard Nov 2000 A
6202806 Sandrin et al. Mar 2001 B1
6223648 Erickson May 2001 B1
6240797 Morishima et al. Jun 2001 B1
6267742 Krivosha et al. Jul 2001 B1
6416703 Kristinsson et al. Jul 2002 B1
6443993 Koniuk Sep 2002 B1
6456884 Kenney Sep 2002 B1
6478826 Phillips et al. Nov 2002 B1
6485776 Janusson et al. Nov 2002 B2
6507757 Swain et al. Jan 2003 B1
6511512 Phillips et al. Jan 2003 B2
6517503 Naft et al. Feb 2003 B1
6589289 Ingimarsson Jul 2003 B2
6592539 Einarsson et al. Jul 2003 B1
6610101 Herr et al. Aug 2003 B2
6626952 Janusson et al. Sep 2003 B2
6666796 MacCready, Jr. Dec 2003 B1
6706364 Janusson et al. Mar 2004 B2
6752774 Townsend et al. Jun 2004 B2
6764520 Deffenbaugh et al. Jul 2004 B2
6811571 Phillips Nov 2004 B1
D503480 Ingimundarson et al. Mar 2005 S
D503802 Bjarnason Apr 2005 S
6887279 Phillips et al. May 2005 B2
6923834 Karason Aug 2005 B2
6936073 Karason Aug 2005 B2
6945947 Ingimundarson et al. Sep 2005 B2
6966882 Horst Nov 2005 B2
6969408 Lecomte et al. Nov 2005 B2
6992455 Kato et al. Jan 2006 B2
7001563 Janusson et al. Feb 2006 B2
7025793 Egilsson Apr 2006 B2
7029500 Martin Apr 2006 B2
7037283 Karason et al. May 2006 B2
D523149 Bjarnason Jun 2006 S
7063727 Phillips et al. Jun 2006 B2
7077818 Ingimundarson et al. Jul 2006 B2
7094058 Einarsson Aug 2006 B2
7094212 Karason et al. Aug 2006 B2
D527825 Ingimundarson et al. Sep 2006 S
D529180 Ingimundarson et al. Sep 2006 S
7101487 Hsu et al. Sep 2006 B2
7105122 Karason Sep 2006 B2
7107180 Karason Sep 2006 B2
7118601 Yasui et al. Oct 2006 B2
7118602 Bjarnason Oct 2006 B2
7136722 Nakamura et al. Nov 2006 B2
D533280 Wyatt et al. Dec 2006 S
7144429 Carstens Dec 2006 B2
7145305 Takenaka et al. Dec 2006 B2
7154017 Sigurjonsson et al. Dec 2006 B2
7161056 Gudnason et al. Jan 2007 B2
7169188 Carstens Jan 2007 B2
7169189 Bjarnason et al. Jan 2007 B2
7169190 Phillips et al. Jan 2007 B2
7198071 Bisbee, III et al. Apr 2007 B2
7198610 Ingimundarson et al. Apr 2007 B2
7217060 Ingimarsson May 2007 B2
7220889 Sigurjonsson et al. May 2007 B2
7223899 Sigurjonsson May 2007 B2
7227050 Sigurjonsson et al. Jun 2007 B2
7230154 Sigurjonsson Jun 2007 B2
7235108 Carstens Jun 2007 B2
7240876 Doubleday et al. Jul 2007 B2
7266910 Ingimundarson Sep 2007 B2
7270644 Ingimundarson Sep 2007 B2
7279009 Herr et al. Oct 2007 B2
7288076 Grim et al. Oct 2007 B2
7295892 Herr et al. Nov 2007 B2
RE39961 Petrofsky et al. Dec 2007 E
7303538 Grim et al. Dec 2007 B2
7304202 Sigurjonsson et al. Dec 2007 B2
7311686 Iglesias et al. Dec 2007 B1
7313463 Herr et al. Dec 2007 B2
D558884 Ingimundarson et al. Jan 2008 S
7335233 Hsu et al. Feb 2008 B2
7347877 Clausen et al. Mar 2008 B2
D567072 Ingimundarson et al. Apr 2008 S
7371262 Lecomte et al. May 2008 B2
7377944 Janusson et al. May 2008 B2
RE40363 Grim et al. Jun 2008 E
7381860 Gudnason et al. Jun 2008 B2
7393364 Martin Jul 2008 B2
7396975 Sigurjonsson et al. Jul 2008 B2
7402721 Sigurjonsson et al. Jul 2008 B2
7411109 Sigurjonsson et al. Aug 2008 B2
D576781 Chang et al. Sep 2008 S
D577828 Ingimundarson et al. Sep 2008 S
7423193 Sigurjonsson et al. Sep 2008 B2
7427297 Patterson et al. Sep 2008 B2
7429253 Shimada et al. Sep 2008 B2
7431708 Sreeramagiri Oct 2008 B2
7431737 Ragnarsdottir et al. Oct 2008 B2
7438843 Asgeirsson Oct 2008 B2
7449005 Pickering et al. Nov 2008 B2
7455696 Bisbee, III et al. Nov 2008 B2
D583956 Chang et al. Dec 2008 S
7459598 Sigurjonsson et al. Dec 2008 B2
7465281 Grim et al. Dec 2008 B2
7465283 Grim et al. Dec 2008 B2
7468471 Sigurjonsson et al. Dec 2008 B2
7470830 Sigurjonsson et al. Dec 2008 B2
7488349 Einarsson Feb 2009 B2
7488864 Sigurjonsson et al. Feb 2009 B2
D588753 Ingimundarson et al. Mar 2009 S
7503937 Asgeirsson et al. Mar 2009 B2
7513880 Ingimundarson et al. Apr 2009 B2
7513881 Grim et al. Apr 2009 B1
D592755 Chang et al. May 2009 S
D592756 Chang et al. May 2009 S
7531006 Clausen et al. May 2009 B2
7531711 Sigurjonsson et al. May 2009 B2
7534220 Cormier et al. May 2009 B2
7544214 Gramnas Jun 2009 B2
7549970 Tweardy Jun 2009 B2
D596301 Campos et al. Jul 2009 S
7578799 Thorsteinsson et al. Aug 2009 B2
7581454 Clausen et al. Sep 2009 B2
7597672 Kruijsen et al. Oct 2009 B2
7597674 Hu et al. Oct 2009 B2
7597675 Ingimundarson et al. Oct 2009 B2
7618463 Oddsson et al. Nov 2009 B2
7632315 Egilsson Dec 2009 B2
7637957 Ragnarsdottir et al. Dec 2009 B2
7637959 Clausen et al. Dec 2009 B2
7641700 Yasui Jan 2010 B2
7650204 Dariush Jan 2010 B2
7662191 Asgeirsson Feb 2010 B2
D611322 Robertson Mar 2010 S
7674212 Kruijsen et al. Mar 2010 B2
7691154 Asgeirsson et al. Apr 2010 B2
7696400 Sigurjonsson et al. Apr 2010 B2
7704218 Einarsson et al. Apr 2010 B2
D616555 Thorgilsdottir et al. May 2010 S
D616556 Hu May 2010 S
7713225 Ingimundarson et al. May 2010 B2
D616996 Thorgilsdottir et al. Jun 2010 S
D616997 Thorgilsdottir et al. Jun 2010 S
D618359 Einarsson Jun 2010 S
7727174 Chang et al. Jun 2010 B2
7731670 Aguirre-Ollinger Jun 2010 B2
7736394 Bedard et al. Jun 2010 B2
7745682 Sigurjonsson et al. Jun 2010 B2
D620124 Einarsson Jul 2010 S
7749183 Ingimundarson et al. Jul 2010 B2
7749281 Egilsson Jul 2010 B2
7762973 Einarsson et al. Jul 2010 B2
7771488 Asgeirsson et al. Aug 2010 B2
7780741 Janusson et al. Aug 2010 B2
7794418 Ingimundarson et al. Sep 2010 B2
7794505 Clausen et al. Sep 2010 B2
7811333 Jonsson et al. Oct 2010 B2
7811334 Ragnarsdottir et al. Oct 2010 B2
D627079 Robertson Nov 2010 S
7833181 Cormier et al. Nov 2010 B2
7842848 Janusson et al. Nov 2010 B2
D628696 Robertson Dec 2010 S
D629115 Robertson Dec 2010 S
7846213 Lecomte et al. Dec 2010 B2
7862620 Clausen et al. Jan 2011 B2
7863797 Calley Jan 2011 B2
7867182 Iglesias et al. Jan 2011 B2
7867284 Bedard Jan 2011 B2
7867285 Clausen et al. Jan 2011 B2
7867286 Einarsson Jan 2011 B2
7868511 Calley Jan 2011 B2
7879110 Phillips Feb 2011 B2
7891258 Clausen et al. Feb 2011 B2
7892195 Grim et al. Feb 2011 B2
D634438 Hu Mar 2011 S
D634852 Hu Mar 2011 S
7896826 Hu et al. Mar 2011 B2
7896827 Ingimundarson et al. Mar 2011 B2
7896927 Clausen et al. Mar 2011 B2
7909884 Egilsson et al. Mar 2011 B2
7910793 Sigurjonsson et al. Mar 2011 B2
7914475 Wyatt et al. Mar 2011 B2
7918765 Kruijsen et al. Apr 2011 B2
D637942 Lee et al. May 2011 S
7935068 Einarsson May 2011 B2
D640380 Tweardy et al. Jun 2011 S
D640381 Tweardy et al. Jun 2011 S
7955398 Bedard et al. Jun 2011 B2
7959589 Sreeramagiri et al. Jun 2011 B2
D641482 Robertson et al. Jul 2011 S
D641483 Robertson et al. Jul 2011 S
7981068 Thorgilsdottir et al. Jul 2011 B2
7985193 Thorsteinsson et al. Jul 2011 B2
7985265 Moser et al. Jul 2011 B2
D643537 Lee Aug 2011 S
7998221 Lecomte et al. Aug 2011 B2
8002724 Hu et al. Aug 2011 B2
8007544 Jonsson et al. Aug 2011 B2
1022480 Clausen at al. Sep 2011 A1
8016781 Ingimundarson et al. Sep 2011 B2
8021317 Arnold et al. Sep 2011 B2
8025632 Einarsson Sep 2011 B2
8025699 Lecomte et al. Sep 2011 B2
8026406 Janusson et al. Sep 2011 B2
D646394 Tweardy et al. Oct 2011 S
D647622 Lee et al. Oct 2011 S
D647623 Thorgilsdottir et al. Oct 2011 S
D647624 Thorgilsdottir et al. Oct 2011 S
1024593 Clausen at al. Oct 2011 A1
8034120 Egilsson et al. Oct 2011 B2
8038636 Thorgilsdottir et al. Oct 2011 B2
8043244 Einarsson et al. Oct 2011 B2
8043245 Campos et al. Oct 2011 B2
8048007 Roy Nov 2011 B2
8048013 Ingimundarson et al. Nov 2011 B2
8048172 Jonsson et al. Nov 2011 B2
8052760 Egilsson et al. Nov 2011 B2
8057550 Clausen et al. Nov 2011 B2
8202325 Albrecht-Laatsch et al. Jun 2012 B2
20010029400 Deffenbaugh et al. Oct 2001 A1
20020052663 Herr et al. May 2002 A1
20020092724 Koleda Jul 2002 A1
20020138153 Koniuk Sep 2002 A1
20030093021 Goffer May 2003 A1
20030125814 Paasivaara et al. Jul 2003 A1
20030139783 Kilgore et al. Jul 2003 A1
20030163206 Yasui et al. Aug 2003 A1
20030195439 Caselnova Oct 2003 A1
20040039454 Herr et al. Feb 2004 A1
20040049290 Bedard Mar 2004 A1
20040054423 Martin Mar 2004 A1
20040064195 Herr Apr 2004 A1
20040088025 Gesotti May 2004 A1
20040181118 Kochamba Sep 2004 A1
20050049652 Tong Mar 2005 A1
20050059908 Bogert Mar 2005 A1
20050070834 Herr et al. Mar 2005 A1
20050085948 Herr et al. Apr 2005 A1
20050155444 Otaki et al. Jul 2005 A1
20060004307 Horst Jan 2006 A1
20060069448 Yasui Mar 2006 A1
20060094989 Scott et al. May 2006 A1
20060135883 Jonsson et al. Jun 2006 A1
20060173552 Roy Aug 2006 A1
20060224246 Clausen et al. Oct 2006 A1
20060249315 Herr et al. Nov 2006 A1
20060258967 Fujil et al. Nov 2006 A1
20060276728 Ashihara et al. Dec 2006 A1
20070016329 Herr et al. Jan 2007 A1
20070043449 Herr et al. Feb 2007 A1
20070123997 Herr et al. May 2007 A1
20070156252 Jonsson et al. Jul 2007 A1
20070162152 Herr et al. Jul 2007 A1
20080114272 Herr et al. May 2008 A1
20080155444 Pannese et al. Jun 2008 A1
20080188907 Aguirre-Ollinger Aug 2008 A1
20090030530 Martin Jan 2009 A1
20090171469 Thorsteinsson et al. Jul 2009 A1
20090222105 Clausen Sep 2009 A1
20100025409 Hunter Feb 2010 A1
20100114329 Casler et al. May 2010 A1
20100179668 Herr et al. Jul 2010 A1
20100312363 Herr et al. Dec 2010 A1
20110210626 Schmidt Sep 2011 A1
Foreign Referenced Citations (8)
Number Date Country
1393866 Mar 2004 EP
WO-03068453 Aug 2003 WO
WO-2004017872 Mar 2004 WO
WO-2004019832 Mar 2004 WO
WO-2006110895 Oct 2006 WO
WO-2009082249 Jul 2009 WO
WO-2010025409 Mar 2010 WO
WO-2010027968 Mar 2010 WO
Non-Patent Literature Citations (265)
Entry
Abbas J. and Chizeck H., “Neural Network Control of Functional Neuromuscular Stimulation Systems: Computer Simulation Studies,” IEEE Transactions on Biomedical Engineering, vol. 42, No. 1, Nov. 1995, pp. 1117-1127.
Abul-haj, C. and Hogan, N., “Functional assessment of control systems for cybernetic elbow prostheses. Part I, Part II,” IEEE Transactions on Biomedical Engineering, vol. 37, No. 11, Nov. 1990, Cambridge, MA, pp. 1025-1047.
Akazawa, K., et. al, “Biomimetic EMG prosthesis-hand,” Proceedings of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, Oct. 1996, Amsterdam, Netherlands, pp. 535-536.
Aminian, “Estimation of Speed and Incline of Walking Using Neural Network,” IEEE Transactions on Biomedical Engineering, vol. 44, No. 3, Jun. 1995, pp. 743-746.
Anderson, F. and Pandy M., “Dynamic optimization of human walking,” Journal of Biomechanical Engineering, vol. 123, Oct. 2001, pp. 381-390.
Andrews, et al., “Hybrid FES Orthosis incorporating closed loop control and sensory feedback,” J. Biomed Eng., vol. 10, Apr. 1988, pp. 189-195.
Arakawa, T. and Fukuda, T., “Natural motion generation of biped locomotion robot using hierarchical trajectory generation method consisting of GA, EP layers,” Proceedings of the 1997 IEEE International Conference on Robotics and Automation, Apr. 1997, Albuquerque, NM, pp. 211-216.
Au., et. al., “Powered Ankle-Foot Prosthesis for the Improvement of Amputee Ambulation,” Proceedings of the 29th Annual International Conference of the IEEE, Aug. 2007, Lyon, France, pp. 3020-3026.
Au, S., “An EMG-position controlled system for an active ankle-foot prosthesis: an initial experimental study,” Proc. of the 2006 IEEE International Conference on Rehabilitation Robotics, Jul. 2005, Chicago, IL, pp. 375-379.
Au, S. and Herr H., “Initial experimental study on dynamic interaction between an amputee and a powered ankle-foot prosthesis,” Workshop on Dynamic Walking: Mechanics and Control of Human and Robot Locomotion, May 2006, Ann Arbor, MI, p. 1.
Au, S., et al. “An ankle-foot emulation system for the study of human walking biomechanics,” Proc. of the 2006 IEEE Int. Conf. on Robotics and Automation, May 2006, Orlando, FL, pp. 2939-2945.
Au, S., et. al., “Biomechanical design of a powered ankle-foot prosthesis,” Proc. of the 2007 IEEE Int. Conf. on Rehabilitation Robotics, Jun. 2007, Noordwijk, Netherlands, pp. 298-303.
Au, S., et. al., “Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits,” Neural Networks, vol. 21, No. 4, Mar. 2008, pp. 654-666.
Au, S., et. al., “Powered Ankle-foot Prosthesis Improves Walking Metabolic Economy,” IEEE Trans. on Robotics, vol. 25, No. 1, Feb. 2009, pp. 51-66.
Barth, D., et. al., “Gait analysis and energy cost of below-knee amputees wearing six different prosthetic feet,” Journal of Prosthetics & Orthotics, vol. 4, No. 2, Winter, 1992, pp. 63-75.
Baten, et al., “Inertial Sensing in Ambulatory back load Estimation,” 18 Annual International Conferences of IEEE Engineering in Medicine and Biology Society, Amsterdam 1996, pp. 497-498.
Bateni, H. and Olney S., “Kinematic and kinetic variations of below-knee amputee gait,” Journal of Prosthetics & Orthotics, vol. 14, No. 1, Mar. 2002, pp. 2-13.
Blaya, J. and Herr, H, “Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, No. 1, Mar. 2004, pp. 24-31.
Blaya, J.A., and Herr, H., “Adaptive Control of a Variable-Impedance Ankle-Foot Orthosis to Assist Drop Foot Gait,” Artificial Intelligence Lab and Harvard-MIT Division Health Sciences and Technology, Boston, MA, 30 pages.
Blaya, J.A., et al., “Active Ankle Foot Orthoses (AAFO),” http://www.ai,mit.edu. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 3 pages.
Blaya, J.A., “Force-Controllable Ankle Foot Orthosis (AFO) to Assist Drop Foot Gait,” submitted to the Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts (Feb. 2003). 88 pages.
Blickhan, R., “The spring-mass model for running and hopping,” J of Biomech. Feb. 22, 1989, Great Britain, pp. 1217-1227.
Bortz, “A New Mathematical Formulation for Strapdown Inertial Navigation,” IEEE Transactions of Aerospace and Electronic Systems, vol. AES-7, No. 1, Jan. 1971, p. 61-66.
Brockway, J., “Derivation of formulae used to calculate energy expenditure in man,” Human Nutrition Clinical Nutrition, vol. 41, Nov. 1987, pp. 463-471.
Brown, R., “On the nature of the fundamental activity of the nervous centres: together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system,” J Physiol, vol. 48, No. 1, Mar. 1914, pp. 18-46.
Chang, et al., Ischemic Colitis and Complications of Constipation Associated with the use of Alosetron Under a Risk Management Plan: Clinical Characteristics, Outcomes, and Incidences the Americal Journal of Gastronenterology, vol. 105, No. 4, Apr. 2010, pp. 866-875.
Chu, A., Kazerooni, H. and Zoss, A., “On the Biomimetic Design of the Berkeley Lower Extremity Exoskeleton (BLEEX),” Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Apr. 2005, Barcelona, Spain, pp. 4356-4363.
Colborne, G. R., S. Naumann, P. E. Langmuir, and D. Berbrayer, “Analysis of mechanical and metabolic factors in the gait of congenital below knee amputees,” Am. J. Phys. Med. Rehabil., vol. 92, pp. 272-278, Oct. 1992.
Collins, et al., “Controlled Energy Storage and Return Prosthesis Reduces Metabolic cost of Walking,” ASB 29th Annual Meeting, Cleveland, Ohio, Jul. 31-Aug. 5, 2005, 1 page.
Collins, et al., “Supporting Online Material for Efficient bipedal robots based on passivedynamic walkers,” Mechanical Engineering, University of Michigan, Feb. 2005, Ann Arbor, MI, pp. 1-8.
Crago P., et. al., “New Control Strategies for neuroprosthetic systems,” Journal of Rehabilitation Research and Development, vol. 33, No. 2, Apr. 1996, pp. 158-172.
Daley, M.A., Felix, G., Biewener, A. A., 2007. Running stability is enhanced by a proximodistal gradient in joint neuromechanical control. J Exp Bioi 210 (Pt 3), Nov. 2006, pp. 383-394.
Dapena, J. and McDonald, C., “Three-dimensional analysis of angular momentum in the hammer throw,” Med. Sci. in Sports Exerc., vol. 21, No. 2, Apr. 1989, pp. 206-220.
Dietz, V., “Proprioception and locomotor disorders,” Nat Rev Neurosci, vol. 3, Oct. 2002, pp. 781-790.
Dietz, V., “Spinal Cord Pattern Generators for Locomotion,” download Feb. 6, 2012, http://www.Clinph-journal.com/article/PIIS1388245703001202/fulltext, 12 pages.
Doerschuk, et. al., “Upper extremity limb function discrimination using EMG signal analysis,” IEEE Transactions on Biomedical Engineering. vol. 30., No. 1., Jan. 1983, pp. 18-28.
Doke, J., et. al., “Mechanics and energetics of swinging the human leg,” The Journal of Experimental Bioloqy, vol. 208, Feb. 2005, pp. 439-445.
Dollar, et al., “Lower Extremity Exoskeletions and Active Orthoses: Challenges and State-of-the-Art,” IEEE Transactions on Robotics, vol. 24, No. 1, Feb. 2008, 15 pages.
Donelan, J., et. al., “Force regulation of ankle extensor muscle activity in freely walking cats,” J Neurophysiol, vol. 101, No. 1, Nov. 2008, pp. 360-371.
Donelan, J., et. al., “Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking,” J. Exp. Bioi., vol. 205, Dec. 2002, pp. 3717-3727.
Donelan, J., et. al. “Simultaneous positive and negative external mechanical work in human walking,” Journal of Biomechanics, vol. 35, Jan. 2002, pp. 117-124.
Drake, C., “Ankle & Foot Splints or Orthoses,” HemiHelp, Information Sheet 13 Last updated Jun. 2009, 5 pages.
Drake, C., “Ankle & Foot Splints or Orthoses (AFOs),” HemiHelp, Last updated Jun. 2009, 8 pages.
Drake, C., “Foot & Ankle Splints or Orthoses,” HemiHelp Information Sheet, London, United Kingdom, 3 pages, www.hemihelp.org.uk/leaflets/hbleaflets90.htm.
Eilenberg, M., “A Neuromuscular-Model Based Control Strategy for Powered Ankle-Foot Prostheses,” Master's Thesis, Massachusetts Institute of Technology, Cambridge, Mass., 2009.
Ekeberg, 0. and Grillner, S., “Simulations of neuromuscular control in lamprey swimming,” Philos Trans R Soc Land B Bioi Sci, vol. 354, May 1999, pp. 895-902.
Ekeberg, 0. and Pearson, K., “Computer simulation of stepping in the hind legs of the cat: an examination of mechanisms regulating the stance-to-swing transition,” J Neurophysiol, vol. 94, No. 6, Jul. 2005, pp. 4256-4268.
Endo, K., et. al., “A quasi-passive model of human leg function in level-ground walking,” Proc. of 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2006, Beijing, China, pp. 4935-4939.
Eppinger, S. Seering W., “Three dynamic problems in robot force control,” IEEE Transactions on Robotics and Automation, vol. 8, No. 6, Dec. 1992, pp. 751-758.
Esquenazi, A. and DiGiacomo, R., “Rehabilitation After Amputation,” Journ Am Podiatr Med Assoc, vol. 91, No. 1, Jan. 2001, pp. 13-22.
Farley, C. and McMahon, T., “Energetics of walking and running: insights from simulated reduced-gravity experiments,” The American Physiological Society, Dec. 1992, pp. 2709-2712.
Farry, K. A., et al., “Myoelectric teleoperation of a complex robotic hand,” IEEE Transactions on Robotics and Automation. vol. 12, No. 5, Oct. 1996, pp. 775-788.
Featherstone, R., 1987, “Robot Dynamic Algorithms”, Boston, Mass., Kluwer Academic Publishers, pp. 155-172.
Fite, K., et. al., “Design and Control of an Electrically Powered Knee Prosthesis,” Proc. of 2007 IEEE 10th International Conference on Rehabilitation Robotics (ICORR), Jun. 2007, pp. 902-905.
Flowers, W. “A Man-Interactive Simulator System for Above-Knee Prosthetic Studies,” Ph.D. thesis, Massachusetts of Institute Technology, Department of Mechanical Engineering. Jul. 10, 1973.
Fod, A., et. al., “Automated Derivation of Primitives for Movements Classification,” Autonomous Robots, vol. 12, No. 1, Jan. 2002, pp. 39-54.
Frigon, A. and Rossignol, S., “Experiments and models of sensorimotor interactions during locomotion,” Bioi Cybern, vol. 95, No. 6, Nov. 2006, pp. 607-627.
Fujita K, et. al., “Joint angle control with command filter for human ankle movement using functional electrical stimulation,” Proc. of IEEE Ninth Annual Conference for the Engineering in Medicine and Biology Society, Nov. 1987, Boston, MA, pp. 1719-1720.
Fukuda, 0. et al., “A human-assisting manipulator teleoperated by EMG signals and arm motions,” IEEE Transactions on Robotics and Automation. vol. 19, No. 2, Apr. 2003, pp. 210-222.
Gates, D., “Characterizing ankle function during stair ascent, descent, and level walking for ankle prosthesis and orthosis design,” Masters thesis, Boston University, 2004, pp. 1-82.
Geiritsen, K., et. al., “Direct dynamics simulation of the impact phase in heel-toe running,” J. Biomech., vol. 28, No. 6, Jun. 1995, Great Britain, pp. 661-668.
Geyer, H., et. al., “Compliant leg behaviour explains the basic dynamics of walking and running,” Proc. R. Soc. Cond. B 273, Aug. 2006, pp. 2861-2867.
Geyer, H., et. al., “Positive force feedback in bouncing gaits?,” Proceedings of Royal Society B-Biological Sciences, vol. 270, No. 1529, Aug. 2003, pp. 2173-2183, 2003.
Geyer, H. and Herr H., “A muscle-reflex model that encodes principles of legged mechanics predicts human walking dynamics and muscle activities,” IEEE Transactions on Neural Systems and Rehabilitations Engineering, vol. 18, No. 3, Jun. 2010, pp. 263-273.
Ghigliazza, R., et. al., “A simply stabilized running model,” SIAM J. Applied. Dynamical Systems, vol. 2, No. 2, May 2004, pp. 187-218.
Godha, el al., “Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment,” ION GNSS, Sep. 2006, Fort Worth, TX, pp. 1-14.
Goswami, A., “Postural stability of biped robots and the foot-rotation indicator (FRI) point,” International Journal of Robotics Research, vol. 18, No. 6, Jun. 1999, pp. 523-533.
Goswami, A. and Kallem, V., “Rate of change of angular momentum and balance maintenance of biped robots,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation, Apr. 2004, New Orleans, La., pp. 3785-3790.
Graupe, D., et al., “A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification,” IEEE Transaction on Automatic Control. vol. AC-23, vol. 4, Aug. 1978, pp. 538-544.
Gregoire, L., and et al, “Role of mono- and bi-articular muscles in explosive movements,” International Journal of Sports Medicine 5, 614-630. Dec. 1984.
Grillner, S. and Zangger, P., “On the central generation of locomotion in the low spinal cat,” Exp Brain Res, vol. 34, No. 2, Jan. 1979, pp. 241-261.
Grimes, D. L., “An active multi-mode above-knee prosthesis controller,” Ph.D. Thesis, Massachusetts Institute of Technology, Jul. 20, 1979.
Gu, W., “The Regulation of Angular Momentum During Human Walking,” Undergraduate Thesis, Massachusetts Institute of Technology, Physics Department, Jun. 2003, pp. 2-48.
Gunther, M., et. al., “Human leg design: optimal axial alignment under constraints,” J. Math. Bioi., vol. 48, Mar. 2004, pp. 623-646.
Gunther, M. and Ruder, H., “Synthesis of two-dimensional human walking: a test of the Amodel,” Bioi. Cybern., vol. 89, May 2003, pp. 89-106.
Hanafusa et al., “A Robot Hand with Elastic Fingers and Its Application to Assembly Process,” pp. 337-359, Robot Motion, Brady et al., MIT Press, Cambridge, MA, 1982.
Hansen, A. H., Childress, D. S., Miff, S.C., Gard, S. A., Mesplay, K. P., “The human ankle during walking: implication for the design of biomimetic ankle prosthesis,” Journal of Biomechanics, vol. 37, No. 10, Oct. 2004, pp. 1467-1474.
Hayes et al., “Leg Motion Analysis During Gait by Multiaxial Accelerometry: Theoretical Foundations and Preliminary Validations,” Journal of Biomechanical Engineering, vol. 105, Aug. 1983, pp. 283-289.
Heglund, N., “A Simple Design for a Force-Plat to Measure Ground Reaction Forces,” J. Exp. Bioi., vol. 93, Aug. 1981, pp. 333-338.
Herr, H. and McMahon, T.,“A trotting horse model,” Int. J. Robotics Res., vol. 19, No. 6, Jun. 2000, pp. 566-581.
Herr, H. and Popovic, M., “Angular momentum regulation in human walking,” J. Exp. Bioi., vol. 211, Feb. 2008, pp. 467-481.
Herr, H. and Wilkenfeld A., “User-adaptive control of a magnetorheologicalprosthetic knee,” Industrial Robot: An International Journal, vol. 30, No. 1, 2003, pp. 42-55.
Herr, H., et. al, “A model of scale effects in mammalian quadrupedal running,” J Exp Bioi 205 (Pt 7), Apr. 2002, pp. 959-967.
Heyn et al., “The Kinematice of the Swing Phase Obtained from Accelerometer and Gyroscope Measurements,” 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Nov. 1996, Amsterdam, Netherlands, pp. 463-464.
Hill, V., “The heat of shortening and the dynamic constants of muscle,” Proceedings of the Royal Society London B, vol. 126, No. 843, Oct. 1938, pp. 136-195.
Hirai, K., et al., “The development of Honda humanoid robot,” Proceedings on IEEE/RSJ International Conference on Intelligent Robots and Systems, May 1998, Leuven, Belgium, pp. 1321-1326.
Hitt, J., R. Bellman, M. Holgate, T. Sugar, and K. Hollander, “The sparky (spring ankle with regenerative kinetics) projects: Design and analysis of a robotic transtibial prosthesis with regenerative kinetics,” in Proc. IEEE Int. Conf. Robot. Autom., Orlando, Fla., pp. 2939-2945, Sep. 2007.
Hof. A., et. al., “Calf muscle moment, work and efficiency in level walking; role of series elasticity,” Journal of Biomechanics, vol. 16, No. 7, Sep. 1983, pp. 523-537.
Hofbaur, M. and Williams, B., “Hybrid Diagnosis with Unknown Behavioral Modes”, Proceedings of the 13.sup.th International Workshop on Principles of Diagnosis (DX02), May 2002, pp. 1-10.
Hofbaur, M. and Williams, B., “Mode Estimation of Probabilistic Hybrid Systems”, HSSC 2002, LNCS 2289, Mar. 25, 2002, pp. 253-266.
Hofmann, A., et. al., “A Sliding Controller for Bipedal Balancing Using Integrated Movement of Contact and Non-Contact Limbs,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 2004, Sendai, Japan, pp. 1952-1959.
Hofmann, A., et. al., “Robust Execution of Bipedal Walking Tasks from Biomechanical Principles,” Doctor of Philosophy at the Massachusetts Institute of Technology, Jan. 2006, 407 pages.
Hogan, N and Buerger S., “Impedance and Interaction Control,” Robotics and Automation Handbook, CRC Press, Jun. 2004, pp. 19.1-19.24.
Hogan, N. (1976) A review of the methods of processing EMG for use as a proportional control signal. Biomedical Engineering. pp. 81-86.
Hogan, N., “Impedance Control: An Approach to Manipulation: Part I—Theory,” Journal of Dynamic Systems, Measurement, and Control, vol. 107, Mar. 1985, pp. 1-7.
Hogan, N., “Impedance Control: An Approach to Manipulation: Part II—Implementation, ” Journal of Dynamic Systems, Measurement , and Control, 107:8-16, (1985).
Hogan, N., Impedance Control: An Approach to Manipulation: Part III—Application, Journal of Dynamics Systems, Measurement, and Control, 107:17-24, (1985).
Hogan, N., “Impedance Control: An Approach to Manipulation,” Dept. of Mechanical Engineering and Laboratory of Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge MA, pp. 304-313, (Jun. 1984).
Hollander, K. W., T. G. Sugar, and D. E. Herring, “Adjustable robotic tendon using a ‘Jack Spring’ .TM.,” Proceedings on IEEE International Conference on Rehabilitation Robotics, Chicago, pp. 113-118, Jun. 28, 2005.
Howard, “Joint and Actuator Design for Enhanced Stability in Robotic Force Control,” Ph.D. thesis, Massachusetts Inst. Of Technology, Dept. of Aeronautics and Astronautics, Sep. 19, 1990.
Huang, H. and Chen. C., “Development of a myoelectric discrimination system for a multi-degree prosthetic hand,” Proceeding of the 1999 IEEE International Conference on Robotics and Automation, May 1999, Detroit, MI, pp. 2392-2397.
Huang, Q., “Planning walking patterns for a biped robot,” IEEE Transactions on Robotics and Automation, vol. 17, No. 3, Jun. 2001, pp. 280-289.
Hultborn, H., Spinal reflexes, mechanisms and concepts: from Eccles to Lundberg and beyond, Prog Neurobiol, vol. 78, Feb. 2006, pp. 215-232.
Ijspeert, A. J., 2008, “Central pattern generators for locomotion control in animals and robots: a review,” Neural Netw, vol. 21, No. 4, May 2008, pp. 642-653.
Ijspeert, A., et. al., “From swimming to walking with a salamander robot driven by a spinal cord model,” Science, vol. 315, No. 5817, Mar. 2007, pp. 1416-1420.
International Search Report and Written Opinion for PCT/US2009/055600 mailed Apr. 29, 2010 (23 pages).
International Search Report and Written Opinion for PCT/US2010/047279 mailed Jan. 19, 2011 (11 pages).
International Search Report and Written Opinion for PCT/US2011/031105 mailed Oct. 11, 2011 (16 pages).
International Search Report for PCT/US2012/020775 mailed Jun. 1, 2012 (6 pages).
International Search Report for PCT/US2012/021084 mailed Aug. 1, 2012 (3 pages).
International Search Report for PCT/US2012/022217 mailed May 31, 2012 (6 pages).
Ivashko, D., et. al, “Modeling the spinal cord neural circuitry controlling cat hindlimb movement during locomotion,” Neurocomputing, vol. 52-54, Mar. 2003, pp. 621-629.
Johansson, J., et al., “A clinical comparison of variable damping and mechanically passive prosthetic knee devices,” American Journal of Physical Medicine & Rehabilitation, vol. 84, No. 8, Aug. 2005, pp. 563-575.
Johnson, C. and Lorenz R., “Experimental identification of friction and its compensation in precise, position controlled mechanisms,” IEEE Trans. on Industry Applications, vol. 28, No. 6, Dec. 1992, pp. 1392-1398.
Jonic S, et. al., “Three machine learning techniques for automatic determination of rules to control locomotion,” IEEE Trans Biomed Eng, vol. 46, No. 3, Mar. 1999, pp. 300-310.
Kadaba, M., et. al., “Measurement of lower extremity kinematics during level walking,” J. Orthop. Res., vol. 8, May 1990, pp. 383-392.
Kadaba, M., et. al., “Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait,” J. Orthop. Res., vol. 7, Nov. 1989, pp. 849-860.
Kajita, K., et. al., “Biped walking on a low friction floor,” Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2004, Sendai, Japan., pp. 3546-3551.
Kajita, S., et. al., “A Hop towards Running Humanoid Biped,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation, Apr. 2004, New Orleans, La., pp. 629-635.
Kajita, S., et. al., “Resolved Momentum Control: Humanoid Motion Planning based on the Linear and Angular Momentum,” Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2003, Las Vegas, Nev., pp. 1644-1650.
Kaneko, K., et al., “Humanoid robot HRP-2,” Proc. IEEE Int. Conf. on Robotics and Automation, Apr. 2004, New Orleans, La., pp. 1083-1090.
Kapti, A. and Yucenur M., “Design and control of an active artificial knee joint,” Mechanism and Machine Theory, vol. 41, Apr. 2006, pp. 1477-1485.
Katie, D. and Vukobratovic, M., “Survey of intelligent control techniques for humanoid robots,” Journal of Intelligent and Robotics Systems, vol. 37, Jun. 2003, pp. 117-141.
Kerrigan, D, et. al., “A refined view of the determinants of gait: significance of heel rise,” Arch. Phys. Med. Rehab., vol. 81, Aug. 2000, pp. 1077-1080.
Kerrigan, D, et. al., “Quantification of pelvic rotation as a determinant of gait,” Arch. Phys. Med. Rehab., vol. 82, Feb. 2001, pp. 217-220.
Khatib, 0., et. al., “Coordination and decentralized cooperation of multiple mobile manipulators,” Journal of Robotic Systems, vol. 13, No. 11, Nov. 1996, pp. 755-764.
Khatib, 0., et. al., “Whole body dynamic behavior and control of human-like robots,” International Journal of Humanoid Robotics, vol. 1, No. 1, Mar. 2004, pp. 29-43.
Kidder, et al., “A System for the Analysis of Foot and Ankle Kinematics During Gait,” IEEE Transactions on Rehabilitation Engineering, vol. 4, No. 1, Mar. 1996, pp. 25-32.
Kim, et al., “Realization of Dynamic Walking for the Humaniod Robot Platform KHR-1,” Advanced Robotics, vol. 18, No. 7, pp. 749-768, (2004).
Kirkwood C, et. al., “Automatic detection of gait events: a case study using inductive learning techniques.,” J Biomed Eng, vol. 11, Nov. 1989, pp. 511-516.
Kitayama, I., Nakagawa N, Amemori K, “A microcomputer controlled intelligent A/K prosthesis,” Proceedings of the 7th' World Congress of the International Society for Prosthetics and Orthotics, Chicago. Jun. 28, 1992.
Klute, et al., Artificial Muscles: Actuators for Lower Limb Prostheses, Abstract in: Proceedings of the 2nd Annual Meeting of the VA rehabilitation Research and Development Service, Feb. 20-22, 2000, p. 107.
Klute, et al., “Artificial Muscles: Actuators for Biorobotic Systems,” The International Journal of Robotics Research, vol. 21, No. 4, Apr. 2002, pp. 295-309.
Klute, et al., “Artificial Tendons: Biomechanical Design Properties for Prosthetic Lower Limbs,” Chicago 2000 World Congress on Medical Physics and Biomedical Engineering, Chicago on Jul. 24-28, 2000, 4 pages.
Klute, et al., Intelligent Transtibial Prostheses with Muscle-Like Actuators,: 2002 American Physiological Society Intersociety Meeting: The Power of Comparative Physiology: Evolution, Integration, and Applied, 1 page.
Klute, et al., “Lower Limb Prostheses Powered by Muscle-Like Pneumatic Actuator,” Submitted to Oleodinamica e Pneumatica, Publishe Tecniche Nuove, Milamo, Italy, Mar. 15, 2000, 6 pages.
Klute, et al., “McKibben Artificial Muscles: Pneumatic Actuators with Biomechanical Intelligence,” IEEE/ASME 1999 International Conference on Advanced Intelligent Mechatronics, Atlanta, GA, Sep. 19-22, 1999, pp. 221-226.
Klute, et al., “Muscle-Like Pneumatic Actuators for Below-Knee Prostheses,” Actuator2000:7th International Conference on New Actuators, Bremen, Germany on Jun. 9-21, 2000, pp. 289-292.
Klute et al., “Powering Lower Limb Prosthestics with Muscle-Like Actuators,” Abstract in: Proceeding of the 1st Annual Meeting of the Va Rehabilitation Research and Development Service, “Enabling Veterans: Meeting the Challenge of Rehabilitation in the Next Millennium,” Washington, D.C., Oct. 1-3, 1998, p. 52.
Klute, et al., “Variable Stiffness Prosthesis for Transtibial Amputees,” Dept of Veteran Affairs, Seattle, WA USA, 2 pages.
Klute, G., et. al., “Mechanical properties of prosthetic limbs adapting to the patient,” Journal of Rehabilitation Research and Development, vol. 38, No. 3, May 2001, pp. 299-307.
Koganezawa, K. and Kato, 1., “Control aspects of artificial leg,” IFAC Control Aspects of Biomedical Engineering, 1987, pp. 71-85.
Kondak, K. and Hommel, G., “Control and online computation of stable movement for biped robots,” Proc. of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2003, Las Vegas, Nev., pp. 874-879.
Kostov A., et. al., “Machine learning in control of functional electrical stimulation (FES) systems for locomotion,” IEEE Trans on Biomed Eng, vol. 42, No. 6, Jun. 1995, pp. 541-551.
Kuo, A., “A simple model of bipedal walking predicts the preferred speed-step length relationship,” Journal of Biomechanical Engineering, vol. 123, Jun. 2001, pp. 264-269.
Kuo, A., “Energetics of actively powered locomotion using the simplest walking model,” Journal of Biomechanical Engineering, vol. 124, Feb. 2002, pp. 113-120.
LaFortune, “Three-Dimensional Acceleration of the Tibia During Walking and Running,” J. Biomechanics, vol. 24, No. 10, 1991, pp. 877-886.
LeBlanc, M. and Dapena, J., “Generation and transfer of angular momentum in the javelin throw,” Presented at the 20th annual meeting of the American Society of Biomechanics, Oct. 1996, Atlanta, Ga., pp. 17-19.
Li, C., et al. (Jun. 25, 2006) Research and development of the intelligently-controlled prosthetic ankle joint. Proc. of IEEE Int. Conf. on Mechatronics and Automation. Luoyang, China, pp. 1114-1119.
Liu, X., Low, K. H., Yu, H. Y., Sep. (2004) ‘Development of a Lower Extremity Exoskeleton for Human performance Enhancement’, IEEE Conf. on Intelligent Robots and Systems, Sendai, Japan.
Light, et. al., Skeletal Transients on Heel Strike in Normal Walking With Different Footwear. J. Biomechanics vol. 13, pp. 477-480.
Lloyd R. and Cooke C., “Kinetic changes associated with load carriage using two rucksack designs,” Ergonomics, vol. 43, No. 9, Sep. 2000, pp. 1331-1341.
Luinge, “Inertial Sensing of Human Movement,” Twente University Press, ISBN 9036518237, 2002, pp. 1-80.
Lundberg, A., Oct. 19, 1968. Reflex control of stepping. In: The Nansen memorial lecture V, Oslo: Universitetsforlaget, 5-42.
Macfarlane, P., “Gait comparisons for below-knee amputees using a flex-foot versus a conventional prosthetic foot,” Journal of Prosthetics & Orthotics, vol. 3, No. 4, Summer, 1991, pp. 150-161.
Maganaris, C., “Force-length characteristics of in vivo human skeletal muscle,” Acta Physiol. Scand., vol. 172, Aug. 2001, pp. 279-285.
Maganaris, C., “Force-length characteristics of the in vivo human gastrocnemius muscle,” Clin. Anal., vol. 16, May 2003, pp. 215-223.
Martens, W.L.J., “Exploring the Information Content and Some Applications of Body Mounted Piezo-Resistive Accelerometers,” in: P.H. Veltink and R.C. van Lummel (eds.), Dynamic Analysis using Body Fixed Sensors, ISBN 90-9007328-0, 1994, pp. 8-11.
Maufroy, C., Towards a general neural controller for quadrupedal locomotion, Neural Netw, vol. 21, No. 4, Apr. 2008, pp. 667-681.
Mayagoitia R., et al., “Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems,” Journal of Biomechanics, vol. 35, Apr. 2002, pp. 537-542.
McFadyen, B. and Winter, D., “An integrated biomechanical analysis of normal stair ascent and descent,” Journal of Biomechanics, vol. 21, No. 9, 1988, Great Britain, pp. 733-744.
McGeer T., “Passive Dynamic Walking,” International Journal of Robotics, vol. 9, No. 2, May 1988, pp. 62-82.
McGeer, T., “Principles of walking and running,” Advances in Comparative and Environmental Physiology, vol. 11, Ch. 4, Apr. 1992, pp. 113-139.
Mcintosh, A., et. al., “Gait dynamics on an inclined walkway,” Journal of Biomechanics, vol. 39, Sep. 2005, pp. 2491-2502.
McMahon, T., “The mechanics of running: how does stiffness couple with speed?,” J. of Biomecb., vol. 23, 1990, pp. 65-78.
McMahon, T., et. al., “Groucho Running,” Journal of Applied Physiology, vol. 62, No. 6, Jun. 1987, pp. 2326-2337.
Minassian, K., et. al., “Human lumbar cord circuitries can be activated by extrinsic tonic input to generate locomotor-like activity,” Hum. Mov. Sci., vol. 26, Mar. 2007, pp. 275-295.
Mochon, S., et. al., “Ballistic walking,” Journal of Biomechanics, vol. 13, Dec. 1980, pp. 49-57.
Molen, N., “Energy/speed relation of below-knee amputees walking on motor-driven treadmill,” Int. Z. Angew. Physio, vol. 31, Mar. 1973, pp. 173.
Morris, “Accelerometry—A Technique for the Measurement of Human Body Movements,” J. Biomechanics, vol. 6, Nov. 1973, pp. 729-736.
Muraoka, T., et. al, “Muscle fiber and tendon length changes in the human vastus lateralis during slow pedaling,” J. Appl. Physiol., vol. 91, Nov. 2001, pp. 2035-2040.
Nakagawa A., “Intelligent Knee Mechanism and the Possibility to Apply the Principle to the Other Joints,” Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vo. 20, No. 5, Oct. 1998, pp. 2282-2287.
Neal R. and Hinton G., “A view of the EM algorithm that justifies incremental, sparse, and other variants,” In Michael I. Jordan (editor), Learning in Graphical Models, 1999, Cambridge, MA, pp. 1-14.
Ng, et al., “Fuzzy Model Identification for Classification of Gait Events in Paraplegics,” IEEE Transactions on Fuzzy Systems, vol. 5, No. 4, Nov. 1997, pp. 536-544.
Nielsen, D., et. al., “Comparison of energy cost and gait efficiency during ambulation in below-knee amputees using different prosthetic feet—a preliminary report,” Journal of Prosthetics & Orthotics, vol. 1, No. 1, 1989, pp. 24-29.
Oda, T, Ketal., 2005, “In vivo length-force relationships on muscle fiver and muscle tendon complex in the tibialis anterior muscle.” Int. J. Sport and Health Sciences 3, 245-252.
Ogihara, N. and Yamazaki, N., “Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model,” Bioi Cybern, vol. 84, No. 1, Jan. 2001, pp. 1-11.
Palmer, M., “Sagittal plane characterization of normal human ankle function across a range of walking gait speeds,” Master's Thesis, MIT, Feb. 2002, Cambridge, MA, pp. 1-71.
Paluska, D., and Herr, H., “Series Elasticity and Actuator Power Ouput,” Proceedings of the 2006 IEEE International Conference on Robotics and Automation, May 2006, Orlando, FL, pp. 1830-1833.
Paluska, D., and Herr, H., “The effect of series elasticity on actuator power and work output: implications for robotic and prosthetic joint design,” Robotics and Autonomous Systems, vol. 54, Jun. 2006, pp. 667-673.
Pang, M., et. al., “The initiation of the swing phase in human infant stepping: importance of hip position and leg loading,” J Physiol, vol. 528, No. 2, Oct. 2000, pp. 389-404.
Pasch, K. A., and W. P. Seering, “On the drive systems for high performance machines,” AMSE J. Mechanisms, Transmissions, and Automation in Design vol. 106, pp. 102-108, Mar. 1984.
Paul, C., et. al., “Development of a human neuro-musculo-skeletal model for investigation of spinal cord injury,” Bioi Cybern, vol. 93, No. 3, Aug. 2005, pp. 153-170.
Pearson, K., “Generating the walking gait: role of sensory feedback,” Prog Brain Res, vol. 143, 2004, pp. 123-129.
Pearson, K., et. al., “Assessing sensory function in locomotor systems using neuro-mechanical simulations,” Trends Neurosci, vol. 29, No. 11, Nov. 2006, pp. 625-631.
Perry, Gait Analysis: Normal and Pathological Function, New Jersey: SLACK Inc.; 1992, Book Review, 1 page.
Perry, J. and S. Shanfield, “Efficiency of dynamic elastic response prosthetic feet,” Journal of Rehabilitation Research and Development, vol. 30, No. 1, 1993 pp. 137-143.
Petrofshy et al., “Feedback Control System for Walking in Man,” Comput. Bioi. Med., vol. 14, No. 2, Mar. 1984, pp. 135-149.
Pfeffer et al., “Experiments with a Dual-Armed, Cooperative, Flexible-Drivetrain Robot System,” Proc. 1993 IEEE Int. Conf. on Robotics & Automation, vol. 3, pp. 601-608, May 5, 1993.
Popovic, et al., “Gait Identification and Recognition Sensor,” Proceedings of 6th Vienna International Workshop on Functional Electrostimulation, Sep. 1998, pp. 1-4.
Popovic, D., “Control of Movement for the Physically Disabled,” Springer-Verlag London Limited, May 2000, pp. 270-302.
Popovic D., et al., “Control Aspects of Active Above-Knee Prosthesis,” Int. Journal Man-Machine Studies, (1991) 35, pp. 751-767.
Popovic, M., “Angular Momentum Primitives for Human Walking: Biomechanics and Control,” Proc. of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 2004, Sendai, Japan., pp. 1685-1691.
Popovic, M., et. al., “Angular Momentum Regulation during human walking: Biomechanics and Control,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation, Apr. 2004, New Orleans, LA, pp. 2405-2411.
Popovic, M., et. al., “Zero spin angular momentum control: definition and applicability,” Proceedings of the IEEE-RAS/RSJ International Conference on Humanoid Robots, Nov. 2004, Los Angeles, CA, pp. 1-16.
Popovic, M., et. al., “Ground Reference Points in Legged Locomotion: Definitions, Biological Trajectories and Control Implications,” International Journal of Robotics Research, Dec. 2006, pp. 79-104.
Popovic, M. and Herr, H., “Global Motion Control and Support Base Planning,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Aug. 2005, Alberta, Canada, pp. 1-8.
Popovic, M.B., W. Gu and H. Herr, “Conservation of Angular Momentum in Human Movement,” MIT Al Laboratory-Research Abstracts, Sep. 2002. pp. 231-232, 2002.
Pratt, G. and Williamson M., “Series elastic actuators,” Proceedings on IEEE/RSJ International Conference on Intelligent Robots and Systems, Jan. 1995, Pittsburgh, PA, pp. 399-406.
Pratt, G., “Legged Robots: What's New Since Raibert,” IEEE Robotics and Automation Magazine, Research Perspectives, Sep. 2000, pp. 15-19.
Pratt, G., “Low Impedance Walking Robots,” Integ. and Camp. Bioi., vol. 42, Feb. 2002, pp. 174-181.
Pratt, J., et. al., “The RoboKnee: An Exoskeleton for Enhancing Strength and Endurance During Walking”, IEEE Conf. on Robotics and Automation, Apr. 2004, New Orleans, LA, pp. 2430-2435.
Prochazka, A. and Yakovenko, S., “The neuromechanical tuning hypothesis,” Prog Brain Res, vol. 165, Oct. 2007, pp. 255-265.
Prochazka, A., et. al., “Positive force feedback control of muscles,” J. of Neuro-phys., vol. 77, Jun. 1997, pp. 3226-3236.
Prochazka, A., et. al., “Sensory control of locomotion: reflexes versus higher-level control,” Adv Exp Med Bioi, vol. 508, 2002, pp. 357-367.
Raibert, M., “Legged Robots that Balance,” The MIT Press, Nov. 1986, Cambridge, MA, p. 89.
Rassier, D., et. al., “Length dependence of active force production in skeletal muscle,” Journal of Applied Physiology, vol. 86, Issue 5, May 1999, pp. 1455-1457.
Riener, R., et. al., “Stair ascent and descent at different inclinations,” Gait Posture, vol. 15, Feb. 2002, pp. 32-44.
Reitman, et. al., Gait analysis in prosthetics: opinions, ideas and conclusions, Prosthetics and Orthotics International, 2002, 26, 50-57.
Robinson, D., “Design and an analysis of series elasticity in closed-loop actuator force control,” Ph.D. Thesis, MIT, Jun. 2000, Cambridge, MA, pp. 1-123.
Robinson, D., “Series elastic actuator development for a biomimetic walking robot,” Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Sep. 1999, pp. 561-568.
Rosen, J., et al., “A myosignal-based powered exoskeleton system,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 31, No. 3, May 2001, pp. 210-222.
Ruina, A., et. al., “A collisional model of the energetic cost of support work qualitatively explains leg sequencing in walking and galloping, pseudo-elastic leg behavior in running and the walk-to-run transition,” Journal of Theoretical Biology, vol. 237, Issue 2, Jun. 2005, pp. 170-192.
Rybak, I., et. al., “Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion,” J Physiol, vol. 577 (Pt 2), Dec. 2001, 617-639.
Sanderson, D., et. al., “Lower extremity kinematic and kinetic adaptations in unilateral below-knee amputees during walking,” Gait and Posture, vol. 6, No. 2, Oct. 1997, pp. 126-136.
Sanger, T., “Human arm movements described by a low-dimensional superposition of principal component,” Journal of NeuroScience, vol. 20, No. 3, Feb. 2000, pp. 1066-1072.
Saranli, U., “RHex: A simple and highly mobile hexapod robot,” Int. Jour. Rob. Res., vol. 20, No. 7, Jul. 2001, pp. 616-631.
Sarrigeorgidis K. and Kyriakopoulos K., “Motion control of the N.T.U.A. robotic snamek on a planar surface,” Proc. of the 1998 IEEE International Conference on Robotics and Automation, May 1998, pp. 2977-2982.
Schaal, S., “Is imitation learning the route to humanoid robots?” Trends in Cognitive Sciences, vol. 3, Jun. 1999, pp. 233-242.
Schaal, S. and Atkeson, C., “Constructive incremental learning from only local information,” Neural Computation, vol. 10, No. 8, Nov. 1998, pp. 2047-2084.
Scott, S. and Winter, D., “Biomechanical model of the human foot: kinematics and kinetics during the stance phase of walking,” J. Biomech., vol. 26, No. 9, Sep. 1993, 1091-1104.
Sentis, L. and 0. Khatib, “Task-Oriented Control of Humanoid Robots Through Prioritization,” IEEE-RAS/RSJ International Conference on Humanoid Robots, Nov. 2004, Santa Monica, CA, pp. 1-16.
Seyfarth, A., “Swing-leg retraction: a simple control model for stable running,” J. Exp. Bioi., vol. 206, Aug. 2003, pp. 2547-2555.
Seyfarth, A., et. al., “A movement criterion for running,” J. of Biomech., vol. 35, May 2002, pp. 649-655.
Seyfarth, A., et. al., “Stable operation of an elastic three-segmented leg,” Bioi.Cybern., vol. 84, 2001, pp. 365-382.
Simon F., et. al, “Convergent force fields organized in the frog's spinal cord,” Journal of NeuroScience, vol. 13, No. 2, Feb. 1993, pp. 467-491.
Sinkjaer, T., et. al., “Major role for sensory feedback in soleus Emg activity in the stance phase of walking in man,” J Physiol, vol. 523, No. 3, Mar. 2000, pp. 817-827.
Skinner, H. and Effeney D., “Gait analysis in amputees,” Am J Phys Med, vol. 64, Apr. 1985, pp. 82-89.
Smidt et al., “An Automated Accelerometry System for Gait Analysis,” J. Biomechanics, vol. 10, 1977, pp. 367-375.
Srinivasan, M., “Energetics of legged locomotion: Why is total metabolic cost proportional to the cost of stance work,” Proc. on ISB XXth Congress and the American Society of Biomechanics Annual Meeting, Jul. 2003, Cleveland, OH, pp. 829.
Stepien, J., et al., “Activity Levels Among Lower-Limb Amputees: Self-Report Versus Step Activity Monitor,” Arch. Phys. Med. Rehabil., vol. 88, No. 7, Jul. 2007, pp. 896-900.
Sugano et al., “Force Control of the Robot Finger Joint equipped with Mechanical Compliance Adjuster,” Proc. of the 1992 IEEE/RSJ Int. Conf. on Intel I. Robots & Sys., Jul. 1992, pp. 2005-2013.
Sugihara, T., et. al., “Realtime Humanoid Motion Generation through ZMP Manipulation based on Inverted Pendulum Control,” Proceedings of the 2002 IEEE International Conference on Robotics and Automation, May 2002, Washington, DC, pp. 1404-1409.
Sup, F., “Design and Control of a Powered Transfemoral Prosthesis,” The International Journal of Robotics Research, vol. 27, No. 2, Feb. 2008, pp. 263-273.
Taga, G., “A model of the neuro-musculo-skeletal system for human locomotion,” Bioi. Cybern., vol. 73, No. 2, Jul. 1995, pp. 97-111.
Takayuki “Biped Locomotion using Multiple Link Virtual Inverted Pendulum Model,” Publication of Electronics Information and Systems Society, vol. 120, No. 2, Feb. 2000, 8 pages.
Thorough man, K. and R. Shadmehr, “Learning of action through adaptive combination of motor primitives,” Nature, vol. 407, Oct. 2000, pp. 742-747.
Tomovic R. et al., “A Finite State Approach to the Synthesis of Bioengineering Control Systems,” IEEE Transactions on Human Factors in Electronics, vol. 7, No. 2, Jun. 1966, pp. 65-69.
Tong, et al., “A Practical Gait Analysis System Using Gyroscopes,” Medical Engineering & Physics, vol. 21, Mar. 1999, pp. 87-94.
Turker, K., “Electromyography: some methodological problems and issues,” Physical Therapy, vol. 73, No. 10, Oct. 1993, pp. 698-710.
van den Bogert, A., “Exotendons for assistance of human locomotion,” Biomedical Engineering Online, Oct. 2003, pp. 1-8.
van den Bogert, et al. “A Method for Inverse Dynamic Analysis Using Accelerometry,” Journal Biomechanics, vol. 29, No. 7, 1996, pp. 949-954.
Veltink P., et al., “The Feasibility of Posture and Movement Detection by Accelerometry,” D-7803-1377-1/93, IEEE, Oct. 1993, pp. 1230-1231.
Vukobratovic M. and Juricic, D., “Contributions to the synthesis of biped gait,” IEEE Transactions on Biomedical Engineering, vol. BME-16, No. 1, Jan. 1969, pp. 1-6.
Vukobratovic M. and Stepanenko J., “Mathematical models of general anthropomorphic systems,” Mathematical Biosciences, vol. 17, Aug. 1973, pp. 191-242.
Walsh, C., “Biomimetic Design of an Under-Actuated Leg Exoskeleton for Load-Carrying Augmentation,” Master's Thesis, MIT, Feb. 2006, pp. 1-94.
Waters, RL., “Energy cost of walking amputees: the influence of level of amputation,” J Bone Joint Surg., vol. 58, No. 1, Jan. 1976, pp. 42-46.
Wilkenfeld, A., “An Auto-Adaptive External Knee Prosthesis,” Artificial Intelligence Laboratory, MIT, Sep. 2000, Cambridge, MA, pp. 1-3.
Wilkenfeld, A. J., “Biologically inspired auto adaptive control of a knee prosthesis,” Ph.D. Thesis, Massachusetts Institute of Technology, Oct. 23, 2000.
Williamson, M., “Series Elastic Actuators,” Artificial Intelligence Laboratory, MIT, Jan. 1995, Cambridge, MA, pp. 1-74.
Willemsen A., et al., “Automatic Stance-Swing Phase Detection from Accelerometer Data for Peroneal Nerve Stimulation,” IEEE Transactions on Human Factors in Electronics, vol. 37, No. 12, Dec. 1990, pp. 1201-1208.
Willemsen A., et al., “Real-Time Gait Assessment Utilizing a New Way of Accelerometry,” Journal of Biomechanics, vol. 23, No. 8, 1990, pp. 859-863.
Williams, B., “Mode Estimation of Model-based Programs: Monitoring Systems with Complex Behavior,” Proceedings of the International Joint Conference on Artificial Intelligence, Aug. 2001, Seattle, WA, pp. 1-7.
Winter, D. A, “Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences,” Clinical Orthopedics and Related Research, vol. 175, May 1983, pp. 147-154.
Winter, D, and Robertson D., “Joint torque and energy patterns in normal gait,” Bioi. Cybem., vol. 29, May 1978, pp. 137-142.
Winter, D. and Sienko S., “Biomechanics of below-knee amputee gait,” Journal of Biomechanics, vol. 21, No. 5, Aug. 1988, pp. 361-367.
Wisse, M., “Essentials of Dynamic Walking, Analysis and Design of two-legged robots,” Ph.D Thesis, Technical University of Delft, 2004, pp. 1-195.
Woodward et al., “Skeletal Accelerations measured during different Exercises,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 207, Jun. 1993, pp. 79-85.
Wu, The Study of Kinematic Transients in Locomotion Using the Integrated Kinematic Sensor, IEEE Transactions on Rehabilitation Engineering, vol. 4, No. 3, Sep. 1996, p. 193-200.
Yakovenko, S., et. al., “Contribution of stretch reflexes to locomotor control: a modeling study,” Bioi Cybern, vol. 90, No. 2, Jan. 2004, pp. 146-155.
Yun X., “Dynamic state feedback control of constrained robot manipulators,” Proc. of the 27th conference on Decision and Control, Dec. 1988, pp. 622-626.
Zlatnik, D., et. al., “Finite-state control of a trans-femoral prosthesis,” IEEE Trans. on Control System Technology, vol. 10, No. 3, May 2002, pp. 408-420.
U.S. Appl. No. 13/347,443, Powered Joint Orthosis, filed Jan. 10, 2012.
U.S. Appl. No. 13/349,216, Controlling Powered Human Augmentation Devices, filed Jan. 12, 2012.
U.S. Appl. No. 13/417,949, Biomimetic Joint Acuators, filed Mar. 12, 2012.
Related Publications (1)
Number Date Country
20120259431 A1 Oct 2012 US
Provisional Applications (1)
Number Date Country
61435045 Jan 2011 US