Many people suffer from diseases or injuries that affect their ability to walk. For example, strokes can result in various primary gait deviations, such as knee hyperextension during stance or limited knee flexion during swing. Secondary gait deviations, which result from compensating for primary gait deviations, can also occur. Hip-hiking is the most common secondary gait deviation. Many of these people can benefit from rehabilitation such as physical therapy to improve or regain their walking ability.
A robotic gait rehabilitation (RGR) training system is provided that facilitates robotic gait retraining of patients, particularly patients experiencing secondary gait deviations such as hip-hiking. The RGR training system includes an actuation system that uses force fields applied to the pelvis of a patient to correct secondary gait deviations in pelvic motion. The system also includes a human-machine or human-robot interface that includes a lower body exoskeleton. The human-robot interface improves torque transmission to the pelvic region. The RGR training system implements impedance control-based human-robot interface modalities that allow a patient to interact with the system in ways that mimic the interaction with a therapist walking along side and manually assisting movement of the patient. Several protocols for use of the RGR training system are provided. The protocols are useful for rehabilitation of patients experiencing secondary gait deviations such as hip-hiking and for research with healthy subjects.
The invention will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings:
The disclosure of U.S. Provisional Patent Application No. 61/500,797, filed on Jun. 24, 2011, is incorporated by reference herein.
Human gait is comprised of strides, which are the intervals between two consecutive heel strikes. See
During normal gait, the pelvis rotates in three planes: frontal, sagittal and transverse. See
The most common primary gait deviation in patients post-stroke is stiff-legged gait. This gait deviation often results in the patient employing secondary gait deviations that involve motor control of the pelvis. Stiff legged gait is associated with hip hiking or circumduction. Hip hiking is an exaggerated elevation of the pelvis on the contralateral side (i.e. hemiparetic side) to allow toe clearance during swing. See
To administer gait retraining therapy, a robotic gait rehabilitation (RGR) training system is provided that generates force fields around a user's pelvis while the user ambulates on a treadmill. The RGR training system in particular targets hip-hiking. One embodiment of an RGR training system 10 is illustrated in
In the embodiment of
Referring more particularly to
The control system (described further below) activates the force field only when the leg on the affected side (the hemiparetic leg) is believed to be in swing. This makes it possible to use only one actuator to generate a well-defined moment around the pelvis in the frontal plane, with a vertical reaction force at the support leg, which is equal in magnitude to the applied force generated by the actuator. In one embodiment, the RGR training system uses a synchronization algorithm, discussed further below, which produces an estimate of the subject's location in their own gait cycle, to control the timing of the actuator.
Referring to
In one embodiment, force generation is suitably achieved via a servo-tube actuator 46, which is a good source of force and lends itself well to impedance control. A suitable servo-tube actuator 46 is Model STA2508, from Copley Controls Inc. (Canton, Mass., USA), which incorporates a direct-drive electromagnetic linear motor, with windings in the actuator housing, and permanent rare-earth magnets in the movable thrust-rod (see
The actuation system 12 of the RGR training system 10 is suspended over the treadmill 22 with a suitable frame 18. In the embodiment of
Referring more particularly to
The frame 18 of the system provides a rigid support for the linear actuation system so that forces can be safely and accurately applied onto a user's pelvis. The frame also provides mounting for body weight support and provides support for upper body, for example, via a handle bar 82. In some embodiments, the frame can also provide unrestricted access for physical therapist from the side to either leg of the subject.
The frame includes structural elements, joined in any suitable manner, for example, with threaded fasteners or by welding. The frame is preferably wide enough for a wheel chair to enter the frame from the rear. The frame can include any suitable height adjustment mechanism 84, such as a pulley and brake winch assembly on the frame uprights, to accommodate users of different heights. The frame elements are fabricated from a suitably strong material, for example, rectangular cross-sectional steel tubing. The frame can be made modular to be easily installed on site.
A handlebar or handlebars 82 can be grasped by the user during gait training. The height and tilt of the handlebars can be adjustable. The handlebars can also be adjustable fore and aft. Quick release adjustment mechanisms can be used, for example, quick release clamps. When the clamps are tightened, the structural rigidity of the frame is enhanced. The adjustment mechanisms can be simplified, for example, with the use of knobs, indexing plungers, and quick-release clamps, to allow the adjustments to be performed by a single person without the need for tools. An emergency stop 86 can also be included, for example, on one of the handlebars.
An embodiment of the human-machine or human-robot interface (HRI) 32 is illustrated in
The HRI addresses these challenges by providing an exoskeleton 34 that improves torque transmission to the pelvic region by employing not only adherence to the pelvic region, but also adherence to the thighs, shanks and feet of a patient. Migration of the pelvic brace 16 is substantially eliminated due to the use of the patient's feet, which are positioned transversely to the action of the applied forces of interest, for anchoring the brace to the body. Alteration of the patient's gait is minimal due to the design of the exoskeleton's hip joints, which allow for hip flexion/extension and abduction/adduction, while still transferring forces through the hip joints to the pelvis. This interface maximizes the effectiveness of force transfer to the pelvis, while minimizing time and effort necessary to don and doff the system.
One embodiment of a human-robot interface (HRI) 32, shown in
The pelvic brace includes a shell 102, formed in two halves, that wraps around and fastens to a user's waist, thereby locating the HRI with respect to the body in the horizontal plane. See
The pelvic brace 16 is coupled to the two leg braces 92 and to the actuation system via four rotational joints. See
Each leg brace includes a thigh component 132, a knee joint 134, a shank component 136, and an ankle brace 138. The thigh component is attachable to a user's thigh in any suitable manner, such as with a thigh strap 142. Similarly, the shank component is attachable to a user's shank in any suitable manner, such as with a shank strap 144.
Referring to
Referring to
Rotational joint assemblies 142, such as that shown in
The knee joint can include an adjustment mechanism 154 of the frontal plane knee angle. An embodiment, illustrated in
The HRI can include load-carrying components that can withstand forces resulting form the structure supporting the full weight of a 244 lb (110 kg) user, which corresponds to a US male in the 99th percentile, with a safety factor of 2. The structural components can be fabricated from, for example, high strength aluminum alloy 7075.
Hardware components of one embodiment of the control system 200 are illustrated in an exploded layout in
An noted above, the RGR training system employs an impedance control system. Impedance control in this context refers to the control of the end-point impedance of a robot or an actuator. Impedance control architecture comprises an inner unity feedback force loop, and an outer unity feedback position loop. The main task of the force loop is to increase backdrivability of the actuator. In that sense, force feedback moves any actuator closer to an ideal source of force. The outer position loop sets the relationship between the position of the end-effector, and the force it exerts. In control theory, this can usually be accomplished with a PD controller, where the proportional term represents virtual spring stiffness, and the derivative term acts like a virtual damper. A simple schematic of an impedance controller is shown in
m
act
{umlaut over (x)}=F
act
−F
ext (1)
The equation describing a simple closed loop control law is:
F
act
=G(Fref−Fext) (2)
These two equations combined give the following equation:
m
act
{umlaut over (x)}=G(Fref−Fext)−Fext (3)
And the transfer function is:
This can be represented by the block diagram in
The immovable mass (body) with stiffness and damping, with Fext being the interaction force between the body and the actuator, can be represented by the first order equation:
F
ext
=B
e
{dot over (x)}+K
e
x (5)
The actuator transfer function (Equation 4) is equated with the body's transfer function (Equation 6) to describe the actuator-body interaction:
where mact/(G+1) is the apparent inertia as experienced by the environment. Therefore, the effect of force feedback is the reduction of the apparent actuator inertia by a factor of G+1.
The impedance controller derivation for controlling the actuator's end point impedance in the RGR training system can be derived as follows, referring to
The equation describing the dynamics is:
m
act
{umlaut over (x)}=F
act
−F
ext (8)
where the force generated by the actuator (Fact) onto the thrust rod is:
F
act
=m
act
{umlaut over (x)}+F
ext (9)
The desired end-point impedance of the actuator thrust rod can be represented by the following equation:
F
ext
=M
c({umlaut over (x)})+Bc({dot over (x)}0−{dot over (x)})+Kc(x0−x) (10)
where Mc is the actuator's apparent mass (inertia), Bc is controller derivative gain (damping) and Kc is controller proportional gain (stiffness).
The desired acceleration of the actuator thrust rod is:
Now substitute the desired acceleration into the actuator force equation:
Equation (13) above describes the impedance controller. Fact is the force command sent to the servo-amplifier. The inertia of the thrust rod mass, mact, should be as low as possible. In practice, the degree to which this apparent inertia can be reduced by use of force feedback is limited. The desired mass Mc is equated to the lowest possible apparent inertia of the thrust rod: Mc=mact/(G+1) and the force controller gain G is selected to be highest possible, while still providing appropriate stability margin. After the substitution, the equation describing force commanded to the actuator Fact is:
F
act=(G+1)[Kc(x0−x)+Bc({dot over (x)}0−{dot over (x)})]−(G)Fext (14)
The above equation lists the constituents of the force command Fact, which is sent into the servo amplifier, to be executed by the actuator. This can be represented by the diagram in
The output of the PD controller, which acts on the position error, can be called the virtual force, Fvirt. It is the output of the virtual spring and virtual damper, Kc and Bc respectively.
Force controller gains are often limited to single digits. At such low gain values, the steady state error can be very significant. For example, using the control law of equation (2) and a proportional gain G=1, the resulting force output Fext is only 50% of the reference Fref. The impedance controller from
As noted above, in one embodiment, the servo tube actuator is equipped with hall-effect sensors, which are used by the servo amplifier to generate an emulated differential quadrature encoder signal (position). The differential encoder position signal from the servo amplifier is converted to single ended using a incremental encoder adapter. The encoder signal is acquired by a data acquisition (DAQ) card, counting both rising and falling edges of the incremental encoder signal (X4 encoding). The net number of counted edges is polled by the controller at 500 Hz and converted to position with knowledge of encoder's resolution (12.5 microns).
The linear potentiometer's signal is low-pass anti-alias filtered (RC 480 Hz cutoff), and acquired by the DAQ at 2 kHz. Pelvic obliquity angle is computed as shown in
The degree to which the actuator system can actually display the specified endpoint impedances depends largely on the extent of backdrivability of the actuator. The higher the backdrivability, the better the system can display the commanded forces. Therefore, proper implementation of force feedback is advantageous for implementation of impedance control.
The signal from the load cell is amplified by an in-line amplifier. An analog anti-aliasing low pass RC filter set with an appropriate cutoff frequency, for example, 480 Hz, can improve the signal quality. This suggests that these attenuated signal components were aliases of higher frequency noise (above 480 Hz). A 4th order inverse Chebyshev filter, for example, with 30 Hz cutoff and a 60 dB attenuation level, conditions the signal further.
Referring again to
As discussed above, the RGR training system applies a moment to the pelvis in the frontal plane, to affect the pelvic obliquity angle. This task requires measurement of the pelvic obliquity angle at all times throughout the gait cycle, as well as measurement of the moment or force exerted onto the user by the RGR training system.
In the field of motion analysis, pelvic obliquity is specified in degrees of angular rotation. To comply with this standard, position feedback is offered to the controller in the same format. The RGR training system uses two linear position measurement units, which are attached to either side of the pelvic brace and operate in the vertical direction. These units are a linear potentiometer and an emulated encoder (internal to the actuator). Position feedback coming from the linear actuator is described above. The pelvic obliquity angle of the pelvic brace is calculated using the relative position of the two attachment points on the pelvic brace (in the vertical direction) and the distance between these two points. Referring to
To apply impedance control at the obliquity level, the control algorithm from the linear-motion case discussed above can be adapted to act on angular position error measured in degrees of the pelvic obliquity angle. This system's block diagram is presented in
This type of approach to gain selection allows fast changes to be made to the force-field strength while the general dynamic properties of the system remain unchanged. The PD gains produce a force command, which is executed by the impedance controller's force control loop. Referring to
Pelvic obliquity reference trajectory is a time series, containing the relationship between space and time. Therefore, in addition to being properly positioned in space, the individual data points of the reference trajectory also have to be presented to the impedance controller at the right time. Therefore, a synchronization algorithm is implemented in the RGR training. One suitable synchronization algorithm is available at D. Aoyagi, W. E. Ichinose, S. J. Harkema, D. J. Reinkensmeyer, and J. E. Bobrow, “A robot and control algorithm that can synchronously assist in naturalistic motion during body-weight-supported gait training following neurologic injury,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 15, pp. 387-400, 2007 (“the Aoyagi synchronization algorithm”).
The duration of a single gait cycle spans between two consecutive left heel strikes. The right heel strike occurs at the 50% mark in the gait cycle (assuming symmetrical gait). The Aoyagi synchronization algorithm estimates the actual temporal position of the subject within his gait cycle based on the angular positions and velocities of the subject's hip and knee joints (8 degrees of freedom). A reference for the synchronization algorithm is constructed by recording an 8-dimensional time series over several gait cycles and finding the normalized mean of each DOF. The 8 DOFs are normalized to ensure that they are assigned equal weight. The reference is generated by the norms of the individual vectors, and is represented by the loop of discrete points in
During operation, a minimization operation of the norm of the difference between the measured 8-dimensional vector and every vector in the reference is performed, and this identifies the location of the nearest neighbor. This result is normalized to give an index value ranging between 0 and 1. This represents the location of the subject in the temporal sense in the gait cycle.
The human-robot interface features knee and hip angle measurement (4 DOFs). Taking derivatives of these signals produces four angular velocities, for a total of 8 DOFs for use in the Aoyagi synchronization algorithm. In addition, a low profile assembly with a micro switch, which is placed in the subject's left shoe, is used to detect left heel strikes. In one embodiment, the micro switch is mounted on an aluminum sheet sized to fit in the shoe and covered with a plastic sheet for user comfort. Knowledge of such a discrete gait event is useful for both generating synchronization reference trajectories, and for synchronization algorithm performance validation purposes.
Signals from the four rotary potentiometers at the hip and knee joints are analog low pass RC-filtered and sent to the data acquisition card. Heel strike signal, which is also collected, is used to parse the data and find 8 means of the 8 DOFs (hip and knee angular positions and velocities) across the multiple gait cycles.
The overall control system architecture is illustrated in
First, the controller can switch between two (or more if necessary) different position references while in operation, within two consecutive gait cycles. The user's hip and knee joint angular positions and velocities are used by the Aoyagi gait estimation algorithm to produce an estimate of the user's point in the gait cycle at any time. This estimation of the point in gait is used in two lookup tables to generate two position references. Switch 1 shown in
The second way to control the force field applied onto the subject is through precise activation and de-activation of the impedance gains. Switch 2 in
The ability to precisely control the timing of force field activation within the gait cycle only when the contralateral leg (the leg on the hemiparetic side of the body due to stroke) is in swing, means that the moments applied onto the pelvis are not indeterminate, despite the fact that only one actuator is used to apply an external force, as shown in
In one embodiment, the servo amplifier employs a Schmitt trigger in its enable function to recognize an “enable” signal (for example, greater than 3.65V) and a “disable” signal (for example, less than 1.35 V). Advantageously, any drive signals sent to the actuator via the servo amplifier should be disabled when the control software or the computer fails, as a safety measure. In one embodiment, a safety circuit (shown in
V
c
=V
0
e
−t/RC (4.18)
Solving for time t:
With R=820 kOhm and C1=C2=4.7 μF, the time for the voltage to drop from maximum 10 V to Schmidt trigger's 3.65 V “on” limit is 3.9 s, and dissipation from 5V takes 1.2 s.
Several protocols for use of the robotic gait rehabilitation training system have been developed to assist patients in overcoming the secondary gait deviation of hip hiking and to study the gait of healthy people. The protocols are used to guide the pelvis in the frontal plane via force fields to alter pelvic obliquity and induce motor adaptations in pelvic obliquity control.
Protocol 1
The RGR trainer system, configured to apply vertical forces on the left side of the body, was programmed to switch between two reference trajectories: baseline and hip-hiking. The switching action was designed to happen quickly but smoothly, occurring when the left leg is in stance (due to the small position error at that time) and following a sigmoid curve at a frequency of 3 Hz. The sigmoid is one half cycle of a 3 Hz sinusoid, minimum to maximum amplitude or vice versa, spanning between 0 and 1.
In this protocol, the user walks at a selected walking speed, such as 1.8 km/h, on the treadmill inside the RGR training system and selects a comfortable cadence at this speed. The actuation system operated under zero force control (back-drivable mode), minimizing interaction forces and allowing for maximum freedom of movement.
Baseline pelvic obliquity and hip and knee joint angles are collected over 100 strides and converted into the baseline pelvic obliquity reference trajectory and synchronization reference respectively, by segmenting the data according to heel strikes (as detected by a foot switch in the subject's left shoe) and averaging across all gait cycles.
Four time epochs are played out to form a continuous run, as outlined in
Epoch 1.
The subject walks freely (back-drivable mode) on the treadmill at the specified speed, for a specified time duration, with a metronome setting the cadence. The actuation system is synchronized to the subject's gait by using the subject's own reference synchronization trajectory (8-DOF).
Epoch 2.
The force field is activated, for a specified time duration, with the subject's own baseline still serving as reference trajectory.
Epoch 3. Adaptation Period.
The reference trajectory is switched from baseline to the hip-hiking pattern, for a specified time duration.
Epoch 4. De-Adaptation Period.
The position reference is switched to subject's own baseline, with the force field still active, for a specified time duration.
Epoch 5.
The force field was switched off (backdrivable mode), for a specified time duration.
Protocol 2
Subject walks at his comfortable walking speed (CWS) on the treadmill inside the RGR training system and selects his own cadence at this speed. The actuation system is operated under zero force control (backdrivable mode), minimizing interaction forces and allowing for maximum freedom of movement.
Baseline pelvic obliquity data and hip and knee joint data are collected and converted into a baseline pelvic obliquity reference trajectory and synchronization reference respectively by segmenting the data according to heel strikes (as detected by a foot switch in the subject's left shoe) and averaging across all gait cycles.
The subject walks again at his comfortable walking speed, while performing a simulated hip-hiking gait pattern. A tunnel can be set around a hip-hiking reference trajectory to make the switch between modes less perceivable by the subject. The tunnel can by implemented in the controller by nullifying the position error while it is less than a particular value (tunnel semi-width), and once the position error surpasses the tunnel semi-width, it is offset by that value.
Four epochs are played out to form a continuous run, as outlined in
Epoch 1.
The subject is allowed to walk freely on the treadmill at their previously found CWS, for a specified number of gait cycles, such as 100.
Epoch 2.
With the subject's baseline pelvic obliquity as the position reference, and with the tunnel size set at 1 degree (half-span), the force field is activated, for a specified number of gait cycles, such as 100.
Epoch 3.
The reference trajectory is switched from the subject's own baseline to the hip-hiking trajectory, for specified number of gait cycles, such as 300.
Epoch 4.
The force field is switched off. This epoch differs from that used in Protocol 1, since the subject is not forced to switch back to own baseline (error clamp), but is given freedom to continue walking with the newly-acquired gait pattern. This epoch is used to record the outcome of gait retraining, which occurred in epoch 3.
In Protocol 2, the outcome measure was the degree of hip-hike in the subject's pelvic obliquity immediately following the hip-hike training epoch.
Protocol 3
The protocol is as follows:
The subject walks at his CWS on the treadmill inside the RGR training system and selects his own cadence at this speed. The actuation system operated under zero force control (backdrivable mode), minimizing interaction forces and allowing for maximum freedom of movement.
Baseline pelvic obliquity data and hip and knee joint data are collected by recording the RGR training system's pelvic brace position measurement and hip and knee angle measurements, and converted into the baseline pelvic obliquity reference trajectory and synchronization reference respectively by segmenting the data according to heel strikes (as detected by a foot switch in the subject's left shoe), and averaging across all gait cycles.
Five different force field levels (for example, Kc=20, 25, 30, 35 and 40 N−m/deg) are randomized. Referring to
Epoch 1.
The subject ambulates, and reaches a steady state pace, for a specified number of gait cycles, such as 50.
Epoch 2.
The subject is exposed to a force field selected randomly out of five different force field levels, with the representative hip-hike pattern serving as the position trajectory, activated between 55% and 85% of the gait cycle, for a specified number of gait cycles, such as 300.
Epoch 3.
A specified number of gait cycles, such as 200, are used to record the outcome of gait retraining.
Epoch 4.
An error-clamp setting, with a tunnel set to +/−0.7 degrees and a force field of K=30 N−m/deg, is used, for a specified number of gait cycles, such as 300.
Epoch 5.
The force field turned is off for a specified number of gait cycles, such as 200. Obliquity from this epoch is used to confirm that de-adaptation is sufficient. One potential method is computing the sample variance s22 and performing an F-test against reference (baseline) variance.
Epochs 2 through 5 are repeated for the other four levels of force field strength. The timing of force field activation (sigmoid switch) is selected to occur after the initial reversal of the pelvis' direction of motion had occurred (from pelvic drop to hip-hike).
Protocol 4
Protocol 4 compares both assistive and resistive training.
During the assistive training, subjects are instructed to follow the guidance of the RGR training system, and during the resistive training, the subjects are instructed to maintain their own natural gait pattern and not to allow the RGR training system to alter it. For each training type, two variations of epoch 3 were used: ‘backdrive’ and ‘playback’. In the backdrive epoch (epoch 3b), the actuation system operates in backdrivable mode, while in the playback epoch (epoch 3p), the mean commanded force profile from the last ten gait cycles of epoch 2 (the epoch immediately preceding epoch 3p) is played back throughout the duration of epoch 3p. Therefore, while the subjects are exposed to a force field in epoch 2, in epoch 3p they are exposed to a constant force profile, which is only a function of the subject's temporal progression through the gait cycle, and not a function of their pelvic obliquity angle.
Each session can include several, for example, three trials, with each trial testing one of three force field magnitudes (such as 5, 15 and 25 N−m/deg), randomized in order. Each trial lasts a selected number of strides, such as 1200, including four 300-stride epochs: hip-hike train (epoch 2), backdrive (epoch 3b) or playback (epoch 3p), error clamp (epoch 4), and backdrive (epoch 5). In protocol 4 a tunnel around the hip-hiking reference is not used. The force field activation switch was set to go on at 44% of the gait cycle (coinciding very closely with toe-off) and to go off at 76% (in order to diminish to zero by left heel strike—the end of the gait cycle).
The subject walks at his CWS on the treadmill inside the RGR training system and selects his own cadence at this speed. The actuation system operates under zero force control (backdrivable mode). A metronome is set to the subject's cadence.
As the subject ambulates for a specified number of gait cycles, such as 100, to the cadence set by the metronome, baseline pelvic obliquity timeseries and hip and knee joint angle time series are collected and converted into the baseline pelvic obliquity reference trajectory and synchronization reference respectively, by segmenting the data according to heel strikes (as detected by a foot switch in the subject's left shoe), and averaging across all gait cycles.
The details of Protocol 4 are as follows, with reference to
Epoch 1.
Initiation: every 5 strides, the RGR training system switches between two operating modes: error clamp (baseline reference) and hip-hike train. This is done to make the subjects accustomed to the operation of the system, and to make subjects believe that there are only two operating modes. The epoch continues for a specified number of gait cycles, such as 50.
Epoch 2.
The subject is exposed to a force field selected randomly out of three force field levels, with the representative hip-hike pattern serving as the position trajectory, activated between 44% and 76% of the gait cycle, for a specified number of gait cycles, such as 300.
Epoch 3b.
The system is operated in backdrivable mode, for a specified number of gait cycles, such as 300.
Epoch 3p.
The system is operated in playback mode, generating a constant force profile (mean commanded force from last 10 gait cycles in epoch 2) as a function of temporal progression through the gait cycle, for a specified number of gait cycles, such as 300.
Epoch 4.
Error-clamp (Kc=15 N−m/deg) with subject's own baseline trajectory is used to de-adapt the subject, for a specified number of gait cycles.
Epoch 5.
The system is operated in backdrive mode, for a specified number of gait cycles, such as 200. Pelvic obliquity during this epoch could be used to confirm de-adaptation.
The RGR training system can incorporate alternative embodiments. For example, besides hip-hiking, another common secondary gait deviation occurring in the motion of the pelvis is circumduction with exaggerated pelvic rotation, as shown in
The system is able to apply corrective moments to pelvic obliquity and pelvic rotation, while allowing close to free translations in the horizontal plane. As is the case with any impedance-controlled device for human interaction, the inertia of the system should be kept to a minimum, in order to enhance the system's ability to display the prescribed force fields. Considering the specific task at hand, the inertia of the system should be less than that of the actuated body part. The body segment inertias can be found using equations in a NASA publication, based on the total body weight (TBW). Anthropometric source book volume I: Anthropometry for Designers (NASA RP-1024), 1978. Static friction can cause the subject to lose balance. Therefore, the maximum allowable static friction force in the horizontal plane was found to be 8.3 N. That same source found the maximum desirable stiffness applied onto the body to be 4150 N/m.
In one embodiment, referring to
Referring to
A further embodiment, illustrated in
A further embodiment is illustrated in
A further embodiment is illustrated in
The system applies force fields in pelvic obliquity, pelvic rotation, and the vertical direction, that is, in three degrees of freedom (DOF). The remaining DOFs are left free. Allowing patients to execute their natural patterns of pelvis translation leads to a feeling of a more natural walk and better control of balance, thus leading to better results for the patients.
More particularly, the RGR training system includes two planar manipulators 344 to apply forces to the right and left sides of the pelvic brace. Each manipulator includes two linear actuators 342. Working in unison, the two manipulators can apply forces (in the vertical direction), moments (applicable to pelvic obliquity and pelvic rotation), or both onto a pelvic brace worn by the patient. In general, this mechanism cannot apply forces in the transverse direction (side to side). As a result, horizontal translations are not actuated. The mechanism can actively respond to the environment's force input under zero-force-control, in order to minimize the interaction force. Thus, the mechanism is back-drivable in the actuated DOFs. Each planar manipulator provides mounting for two linear actuators, which pivot about their center of housing in order to minimize their moments of inertia. The linear actuators are suspended on ball bearings to reduce friction.
The following equations describe kinematics of the left-hand side closed link mechanism. The angle βL is found from the following equation:
Now using β, the distance from the endpoint to the vertical axis can be found:
c
L
=b
L sin βL
The angle of rotation is measured directly (α), so that vector p can be described, which locates the mechanism's endpoint with respect to the origin xyz.
The position of the endpoint on the right-hand side is found in the same exact way, giving two vectors, which describe the locations of P′L and P′R with respect to the two origins located on either side of the device. Next, the locations of these two points are found with respect to the default location of the subject in the system, as shown in
Now we employ translation in order to find the position vectors of points PL and PR with respect to the main reference frame (xyz):
The pelvic rotation θ and pelvic obliquity φ angles are found using the above position vectors:
The two force vectors necessary to impart the desired moments Tθ and Tφ onto the pelvis are found as follows:
Now substitute:
and solve above equation for |fi|.
Thus the magnitudes of the force commands are obtained, as shown in the equation above, which should be sent to the two actuators of a planar manipulator in order to produce the required force f, as is shown in
This embodiment expands the functionality of the RGR training system, by providing the ability to apply corrective torques to pelvic rotation. Abduction combined with exaggerated pelvic rotation is the second most common secondary gait deviation in control of the pelvis, after hip-hiking, and therefore in general it is desirable to be able to address this particular gait deviation in the future.
The planar manipulators 344 are mounted to a frame 350, illustrated in
The frame can include a handlebar 358 for the patient to grasp while using the system. The handlebar can include a height adjustment mechanism 362. One embodiment of a height adjustment mechanism, illustrated in
The present system incorporates the advantageous qualities of high back drivability and force controllability, with impedance control. The control system is able to modulate the forces applied onto the body depending on the patient's efforts. The system allows all of the natural motions of the pelvis and features a lower body exoskeleton that employs the waist, thighs, shanks, and feet to transfer moments to the pelvis. The system, incorporating the lower body exoskeleton, highly backdrivable linear actuator, impedance control and a gait synchronization algorithm, produce a gait retraining system that can effectively and reliably apply corrective moments to pelvic obliquity. The actuation system and human-robot interface of the trainer are simple, with low moving mass and low friction, easing the task of the control system in generating appropriate performance of the overall system. The present system leaves translation in the horizontal plane un-actuated and as friction-free as possible, leading to improved gait
It will be appreciated that components used in the system can be analog or digital. For example, the linear potentiometer described above can be replace with a digital linear encoder to eliminate noise inherent to analog devices. The control system can be implemented in any suitable manner, as can be appreciated by those of skill in the art. Also, those of skill in the art will recognize that various features described in conjunction with one embodiment can be used in conjunction with other embodiments.
The RGR training system can also operate in conjunction with a powered knee orthotic device. This combination can be used to administer gait rehabilitation therapy by addressing both primary and secondary gait deviations, exhibited in the knee joint and the pelvic motion respectively. For example, a powered knee orthotic device can be worn on the affected side, preventing stiff-legged gait, which is the primary gait deviation, which in turn leads to hip-hiking, a secondary gait deviation in the pelvic motion.
The system can also be used for studying gait in healthy people, which may lead to developing better gain retraining therapies for post-stroke patients.
The invention is not to be limited by what has been particularly shown and described, except as indicated by the appended claims.
This application claims priority under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 61/500,797, filed on Jun. 24, 2011, the disclosure of which is incorporated by reference herein.
This invention was made under National Science Foundation NSF Grant No. 0803622. The Government may have certain rights in the invention.
Filing Document | Filing Date | Country | Kind | 371c Date |
---|---|---|---|---|
PCT/US12/44019 | 6/25/2012 | WO | 00 | 3/27/2014 |
Number | Date | Country | |
---|---|---|---|
61500797 | Jun 2011 | US |