Patent Application
20130023798
1. Technical Field
Embodiments generally relate to prospectively assessing falls risk.
2. Discussion
Falls in the elderly may represent a substantial health care problem worldwide. Indeed, a significant percentage of people over seventy years of age experience a significant fall, and the frequency of falls increases with age and the level of frailty. The timed up and go (TUG) test was developed as a tool to screen for balance problems in older individuals. In the TUG test, the individual gets up from a chair, walks three meters, turns at a designated spot, returns to the seat and sits down, wherein the total time taken to perform the test may generally be considered as indicative of the frailty of the individual. While it may be generally inferred that elders with longer TUG times can be more likely to fall than those with shorter TUG times, there still remains considerable room for improvement with regard to the use of the TUG test to conduct falls risk assessments.
The various features of the embodiments will become apparent to one skilled in the art by reading the following specification and appended claims, and by referencing the following drawings, in which:
Embodiments of this invention relate to generating models that assess a person's risk of falling and finding the optimum set of parameters and parameter values for the models.
The parameters may be generated from sensors, such as gyroscopes and accelerometers, that are attached to a person's body, such as to one or both of the person's shanks, and that generate angular velocity data as the person moves. Some embodiments generate such data through conducting a timed up and go (TUG) test for a person. The parameters that may be generated and derived are discussed below in more detail and may be seen in
Classifier models may be generated to estimate risk of falling for all test participants, for a subset of test participants, or for a particular participant. The classifier models do not need to depend on all the derived parameters. Sequential forward feature selection may be used to identify an optimal set of parameters for the classifier models. A grid search may be used to identify optimal values for the parameters.
The classifier models may be trained using the actual occurrence of falls experienced by the test participants associated with the model. In a retrospective approach, the occurrence of falls may be based on a test participant's self reported falls history. Because such reports may be unreliable, however, a prospective approach may instead or in addition be used in which test participants are later asked if they experienced a fall. For example, after the original TUG test, test participants may be contacted and surveyed as to whether they experienced a fall and the number of falls they experienced. This data can be used to train the classifier models. The prospective approach may be used when more reliability in the training data is desired.
The resulting trained classifier models produce an estimate of falls risk, but their selected feature sets may also indicate particular parameters that affect the specific participants associated with the model. Because such parameters may relate to specific physical, sensory, or other deficits in a participant's movement, the parameters may allow a more targeted diagnosis and treatment to be applied to a participant seeking to lower the risk of falling.
Embodiments may provide for a system including a plurality of inertial sensors to be coupled to a corresponding plurality of shanks of an individual, a processor, and memory to store a set of instructions. If executed by the processor, the instructions cause the system to calculate a timed up and go (TUG) time segment (discussed below in more detail) based on angular velocity data from the plurality of inertial sensors, and calculate a derived parameter based on the angular velocity data, which may be based on a measure of rotation about X, Y, and Z gyroscope sensor axes, as illustrated in
Embodiments may also provide for a computer readable storage medium including a set of instructions which, if executed by a processor, cause a computer to calculate a TUG time segment based on angular velocity vectors from the plurality of inertial sensors, and calculate a derived parameter based on the angular velocity vectors.
Other embodiments can involve a method of conducting falls risk assessments in which a plurality of adaptive thresholds are calculated based on angular velocity data from a plurality of shank-mounted inertial (also referred to as kinematic sensors). Inertial sensors are a subset of kinematic sensors, and the method may work for all forms of kinematic sensors where applicable. A plurality of initial contact points (such as heel strike points) and terminal contact points (such as toe-off points) may be detected based on the angular velocity data.
The TUG time segment that may be based on the angular velocity data includes at least one of a walk time, a turn time, and a return time. The walk time can identify an amount of time between a first step and a last step of a TUG test; the turn time can identify an amount of time between the first step and a turn step of the TUG test; and the return time can identify an amount of time between the turn step and the last step of the TUG test. Calculation of the TUG time segment is discussed in more detail below.
In addition, the method may involve calculating a derived parameter based on the angular velocity data. The derived parameter includes at least one of a temporal gait parameter, a tri-axial angular velocity-based parameter, a spatial gait parameter, and a turn parameter. The four categories of parameters are discussed below in more detail.
Additional parameters may also be obtained. For example, a test participant's stride length, stride velocity, and the coefficient of variation of both parameters may be derived based on, for example, gyroscopes attached to each of the participant's legs. In another example, parameters that measure the participant's ability to turn may also be derived. These parameters are discussed below in more detail. In another example, a participant's grip strength may be measured, such as with a handheld dynamometer. A participant's eyesight may also be measured, such as on a Binocular logmar or a Pelli-Robson contrast sensitivity scale. The participant's age and weight may also be recorded.
Each sensor 14, which might be mounted to the corresponding shank 16 below the patella via a tight fitting piece of clothing, a sock, an elastic tubular bandage, embedded in a shoe, or any other method of attachment that can yield a clear tri-axial angular velocity signal, may include a tri-axial accelerometer and an add-on tri-axial gyroscope board. In particular, each sensor 14 may be positioned such that its measuring axis is aligned with the medio-lateral axis of the corresponding shank 16, and so that it is about half-way along the imaginary line between the Tibial Tuberosity (TT) and the Lateral Malleoulus (LM). In order to ensure that the angular velocity signal derived from the gyroscope has the correct polarity, the “skewness” of the signal (e.g., a measure of the asymmetry of the signal) may be calculated for each walk. If the skewness is less than zero, the gyroscope signal can be inverted to ensure the correct polarity of the signal. The sensors 14 may be programmed to sample each axis at a particular rate (e.g., 102.4 Hz) using firmware (e.g., TinyOS) or other programmable technique, and to wirelessly transmit the angular velocity data using a protocol such as a low-rate wireless PAN (personal area network) or Bluetooth protocol. Data may be streamed to various platforms, such as a desktop computer, laptop, or mobile device (e.g., a cellular phone).
Turning now to
In the illustrated example, gyroscope data can be obtained using any appropriate mode of kinematic data acquisition. Upon receipt of the gyroscope data 20, processing block 22 may provide for using a sensor-to-segment offset orientation matrix (e.g., a rotation matrix) to calibrate the data 20 to derive acceleration and angular velocity vectors with respect to the coordinate axis of each inertial sensor. Illustrated block 24 applies a low pass filter (LPF) to the calibrated data. In one example, the LPF might include a zero-phase 5th order Butterworth filter (e.g., 20 Hz corner frequency or 50.2 Hz corner frequency).
With reference to
For example,
Returning to
Mid-swing point for each gait cycle: valid local maximum peaks may be required to have a preceding minimum of at least th1 rad/sec less than the maximum medio lateral angular velocity (ωML), wherein th1 can be calculated as,
th
1=0.6·max(ωML) (1)
In addition, valid local maximum peaks can be required to be greater than th2 rad/sec, wherein th2 may be calculated as,
Moreover, if two maximum peaks are found within t1 seconds of each other, only the greater maximum can be considered, wherein t1 may be defined as, for example, 0.5 seconds or fs*1.5 and fs is defined as the stride frequency.
Initial contact points: valid local minimums may be required to have a preceding maximum of at least th3 rad/sec greater than the local minimum, wherein th3 can be calculated as,
In addition, valid local minimums could be required to be less than th5, wherein th5 might be defined as,
th5=mean(ωML) (4)
Terminal contact points: valid local minimums can be required to be less than th4, wherein th4 may be calculated as,
In addition, valid local minimums could be required to have a preceding maximum of at least th6 greater than the local minimum, wherein th6 might be defined as,
th6=2th3 (6)
Initial contacts and terminal contacts: following mid-point detection, only data within t2 seconds may be considered, wherein t2 can be defined as, for example, 1.5 seconds or fs*1.5. Specific values and ranges are provided herein to facilitate discussion only, and other values and ranges may be used as appropriate.
Block 34 may provide for detecting initial contact and terminal contact points based on the adaptive thresholds, as already discussed.
One or more quantitative TUG time segments can be calculated at block 36. The quantitative TUG time segments could include the walk time, the turn time and/or the return time. The walk time may identify the amount of time between the first step and the last step of the TUG test. The first step can be defined by at least one of the first initial contact point and the first terminal contact point, and the last step can be defined by at least one of the last initial contact point and the last terminal contact point. The turn time can identify the amount of time between the first step and the turn step of the TUG test. The turn step may be defined by at least one of a turn initial contact point and a turn terminal contact point. A lower ML angular velocity signal or a large positive peak in the vertical angular velocity may indicate that a TUG participant was turning at that point. In one example, a per-shank turn time is calculated for each shank as the time of the median detected gait point (terminal contact, heel-strike, mid-swing), and an overall turn time is calculated as the mean of the per-shank turn times. The return time may identify the amount of time between the turn step and the last step of the TUG test. Thus, the walk, turn and return times can be considered as time “segments” in that each calculation is a portion of the traditional quantitative TUG time, which is the entire amount of time required for the individual to complete the TUG test. The walk time, turn time and return time can be strong indicators of falls risk.
The turn time may also be used to examine the turning phase of the TUG test. The ML angular velocity signal may be automatically segmented into a walking section and a turning section. If the amplitude of a given mid-swing point was more than one standard deviation below the mean amplitude of all mid-swing points, it may be considered part of the turn.
In addition to the TUG time segments, one or more derived parameters may also be determined. For example, block 38 demonstrates that the derived parameters could include various other temporal gait parameters. Examples of such temporal gait parameters include, but are not limited to, stride velocity, stride length, the number of gait cycles, the number of steps taken, cadence, step time, stride time, stance time, swing time, single support percentage, and double support percentage.
The stride length may be defined as the distance covered in a stride time. The stride time may include the time recorded between successive initial contact points (e.g., between successive heel strikes). The distance may be calculated as the distance covered during the swing phase of the gait cycle, which encompasses the time between a terminal contact point and a subsequent initial contact point. The stride length may be modeled as SxHx√{square root over (2(1−cos θ))}, where S represents a scaling factor to be optimized during calibration, H represents the participant's height, and θ represents the range of angular displacement in the sagittal plane during the stride.
The number of gait cycles can be calculated as the number of initial contact points detected from the angular velocity signal during the TUG test minus one (i.e., the number of complete gait cycles).
The cadence (e.g., steps per minute) can be calculated as sixty times the number of steps taken while performing the TUG test divided by the walk time (e.g., time taken to take the steps identified during the TUG test).
Step time can be calculated as the time between the initial contact point on one foot and the initial contact point on the other foot. Stride time can be calculated from the time from initial contact (e.g., initial contact) of one foot to initial contact of the same foot.
Stance time can be calculated from the time between a initial contact and a terminal contact point on the same foot. Swing time can be calculated from the time between a terminal contact point and a initial contact point on the same foot. Double support may be determined by calculating the percentage of each gait cycle during which both feet are in contact with the ground (where the gait cycle time can be the time between successive right initial contacts). As will be discussed below, the number of gait cycles, number of steps taken, cadence, double support percentage and step time can all be strong indicators of falls risk either alone or in combination with one or more other effects.
Other temporal gait parameters that may be derived include single support variability, step time variability, swing time variability, walk-turn time ratio, TUG recording time, walk time, turn time, and return time.
Single support percentage for a foot may be defined as the swing duration of the other foot expressed as a percentage of gait cycle time, where the single support percentage data for each foot may be merged. The coefficient of variability (CV) for the single support percentage (as well as the other temporal gait parameters) can be calculated as a measure of single support variability. Thus, a “CV single support” parameter (expressed as a percentage) could be defined as the ratio of the standard deviation to the mean of the single support percentage. Similarly, a “CV step time” parameter may be calculated to reflect the step time variability as the ratio of the standard deviation to the mean of the step time.
The swing time can be calculated as the time between a terminal contact (TC) point and the initial contact (IC) point on the same foot. Thus, the swing time variability (“CV swing time”) could be expressed as the ratio of the standard deviation to the mean of the swing time. The walk-turn time ratio could be defined as the ratio of the time to turn to the time from turn (e.g., unity indicates the same time taken to walk to and from the turn). The single support variability, step time variability and walk-turn time ratio may be indicators of falls risk, particularly if combined with one or more other effects. The TUG recording time may be calculated from the duration of the edited data recording for each TUG test.
Block 40 demonstrates that in addition to the temporal gait parameters, the derived parameters may include one or more parameters that are obtained directly from the angular velocity signal in the Y, X, and Z directions in order to capture characteristics of the signal during the TUG test in three dimensions.
The tri-axial set of angular velocities may also be multiplied by the height of the individual performing the TUG test in order to obtain a variable approximately proportional to the linear velocity of the shank. This approximation can be based on the formula for linear velocity, which equals the radius times angular velocity, wherein the radius is the leg length and height is assumed to be approximately proportional to the leg length. Thus, the linear velocity may be specifically related to the shank/foot of the individual as opposed to merely the trunk of the individual.
A mid-swing point and mean amplitude may also be calculated. The mean amplitude of the mid-swing points can be calculated as the mean angular velocity at each of the mid-swing points, while the range of mid-swing points may be defined as the difference in amplitude (in deg/s) between the largest and smallest mid-swing points on the angular velocity signal obtained for each shank Thus, the range of mid-swing point amplitudes may capture variability in leg movement.
The walk angular velocity, linear velocity and mid-swing point amplitude parameters can be strong indicators of falls risk either alone or in combination with one or more other effects. In addition, other angular velocity-based parameters such as turn angular velocity may be calculated. The turn angular velocity can be defined as the mean amplitude (taken across both shanks) of the angular velocity signal at the turn point for each shank. The turn angular velocity may be an indicator of falls risk, particularly if combined with one or more other effects. The coefficient of variation (CV) may also be calculated for each angular velocity parameter in order to provide a measure of variation during the TUG test. The CV may be calculated as a ratio of the standard deviation of the parameter measurements to the mean of the parameter measurements, expressed as a percentage. A non-exhaustive list of parameters that may be collected and derived is summarized in
As already discussed, the temporal gait parameters and TUG time segments may be calculated from the gait characteristic points such as initial contact and terminal contact points. An artefact rejection routine may be employed at block 42 to remove spurious temporal parameters that might have been calculated from erroneous gyroscope data. The artefact rejection routine can also be designed to account for missing and extra IC and TC points detected by the adaptive TUG algorithm. Artefact rejection may be based on two strands: examining temporal sequence information, and examining times between successive characteristic points (e.g., “gait cycle information”).
Temporal sequence information may be obtained based on the following: once all characteristic points are detected in processing block 34, each point may be assigned a numerical label of one to four—1-right heel-strike, 2-left terminal contact, 3-left heel-strike, 4-right terminal contact. A correct gait cycle (if starting on a right initial contact) would then follow the sequence 1, 2, 3, 4. By subtracting each label from the previous label, spurious samples (e.g., samples not producing a difference equal to either −3 or 1) may be deemed artefacts and rejected.
Gait cycle information may be obtained based on the following: the time between adjacent gait characteristic points may be calculated for each set of characteristic points (e.g., right IC, left TC, left IC, right TC). This calculation can be referred to as “gait cycle time”. If the difference between any successive characteristic point is greater than a particular time threshold (e.g., 2.5 seconds), the associated characteristic point could be identified as an artefact. Similarly, if the difference between any successive characteristic point is zero seconds, the associated point may be flagged as an artefact. Furthermore, any gait parameters with a negative or zero value may also be rejected. The result may be a set of TUG parameters 44 that are highly reliable and can be used to effectively generate falls risk assessments.
Any video data for each test participant's TUG test may also be visually inspected to ensure that only data from valid TUG tests are included in assessing falls risk.
The gait and balance of community dwelling elderly adults, for example, may be assessed using shank-mounted inertial sensors while each of the adults perform the TUG test. Individuals may also be evaluated using the Berg balance scale (BBS), and the above-described TUG time segments and other derived parameters may be collected or calculated based on the angular velocity data from the inertial sensors, as discussed above. Table I below shows example mean and ranges of some of the parameters collected from test participants. The participants in the example involved 349 participants, consisting of 103 male participants and 246 female participants. The data analyzed were acquired from 207 participants with a self-reported history of falling and 142 participants without a self-reported history of falling. 65 of the participants had two or more falls in the previous year. 119 participants had no history of falls. The mean age of the participants was 72.4±7.4 years of age, and the mean weight was 73.7±14.5 kg. The manual TUG data shows strong variation with age. While manual TUG time in the example increased with age for both genders, and was longer for fallers than non-fallers, the differences between fallers and non-fallers did not vary with age. There was statistically significant difference between fallers and non-fallers in maximum grip strength over the whole group as well as in the male and females under 75 groups. Contrast sensitivity was also significantly different between fallers and non-fallers overall and in the male group. Binocular logmar, on the other hand, was not found to be significant overall in any of the sub-groups.
Generally, the quantitative TUG time segment parameters may be strongly correlated with the manual TUG time, including return time (ρ=0.89, p<0.001), time of turn (ρ=0.83, p<0.001) and walk time (ρ=0.90, p<0.001). The parameters may have significance only in combination with another effect. Those parameters may therefore contain complementary information about the properties of standing, turning and walking associated with falls that are not captured by the BBS and manual TUG tests.
Generating classifier models based on the collected and calculated parameters may require the models to be trained based on the occurrence of falls experienced by the test participants. This may be done using a retrospective or a prospective approach.
In the retrospective approach, data on a participant's prior falls history may be collected, such as in the previous five years. A fall may be considered as a sudden, unintentional change in position causing an individual to land at a lower level, on an object, on the floor, on the ground, or other surface. A fall may be more generally considered as a loss in balance or a change in position that causes a person to drop toward the ground. In the retrospective approach, a faller may include participants who experienced a threshold number of falls, such as two or more falls in the past five years. A faller may additionally include participants who experienced certain risk factors during a fall, including syncope, presyncope or loss of consciousness; dizziness or light-headedness; chronic pain; injuries after falling; fear of falling; depression after falling, or some other characteristic related to falling. A participant who had an accidental fall without risk factors would be classified as a non-faller. For the classifier models, the participants may be classified, for example into those who are not at risk (no falls in last five years), those who are at risk (no falls but has problems with balance and walking), Faller 12-months (one fall in previous 12 months), Faller 6-months (one fall in previous six months) and Recurrent Faller (more than two falls in previous 12 months)
In the prospective approach, test participants may be contacted after their initial baseline assessment to collect falls data. For example, each participant may be contacted within two years of the initial baseline assessment to determine whether the participant had fallen during that time span. Participants with two or more falls in the follow-up period may be deemed recurrent fallers. The prospective approach may take more time to acquire data related to the occurrence of falls, but may be more clinically relevant and reliable than the retrospective approach because the self-reported falls collected during a retrospective approach may be unreliable.
An initial study may generate TUG-derived parameters and use self-reported falls history to generate a retrospective falls risk estimate. A prospective approach may follow up with the test participants to determine which participants have experienced a fall after the initial study, and how many times they have fallen. In this prospective approach, the new data may be used to train a predictive classifier model to generate a falls risk estimate.
To identify those parameters of specific importance to an estimation of falls risk, an optional initial non-parametric screening may be performed. For example, the Mann-Whitney version of the Wilcoxon rank-sum test may be used to test for statistical differences between subjects who experienced a fall and those who did not. The Wilcoxon rank-sum test may test for statistical differences in each variable.
Following initial non-parametric screening, regularized discriminant (RD) statistical classifier models may be used to generate models for predicting risk of falling. Classifier models such as a linear discriminant classifier model, a quadratic discriminant classifier model, or a regularized discriminant classifier model may be used. A linear discriminant classifier model, for example, may be used if there is enough data to calculate a common covariance matrix. In general, the data set may be divided into classes. A class conditional mean vector uk may then be generated to calculate a covariance matrix and then a discriminant for the class. The class conditional mean vector uk may be calculated as
where xnk is the nth feature vector in class k, N is the total number of feature vectors, and uk is the class conditional mean vector. The class conditional mean vector may be used to calculate a common covariance matrix Σ:
where c is the number of pattern classes.
The common covariance matrix may be used to calculate the discriminant yk(x):
yk(x)=−½μkTΣ−1μk+μkTΣ−1x+log(πk), where x is the feature vector and πk is the prior probability.
A quadratic discriminant classifier model may also be used. The covariance matrix for each class Σk of the model may be calculated as
The covariance matrix may be used to calculate a quadratic discriminant for each class:
y
k(x)=−(x−μk)TΣ−1(x−μk)+2 log(πk)−log|Σk−1|
The class with the largest discriminant for all classifier models may be used as the final class.
In pattern recognition problems with small data sizes (i.e., small number of test participants) and a large feature set, however, some of the parameters are not always identifiable from the data because the covariance matrix can be singular (with zero or infinitesimal eigenvalues). Because such matrices are non-invertible, a linear discriminant model, as described above, may not be obtainable. Such problem is said to be ill posed. Regularization may be used as a solution by biasing the data set away from their sample values toward more physically plausible values. Methods for performing the regularization may be found in, for example, Combining pattern classifiers: methods and algorithms, by Kuncheva. There are two methods, which may be combined, for stabilizing the covariance matrix. In a first method, regularization may be performed towards common covariance matrix with parameters λ:
Σk(λ)=(1−λ)Σk+λΣ, where Σk is an estimate of the covariance matrix for a class k and E is the common covariance matrix.
In a second method, regularization may further be performed towards the diagonal matrix (with eigenvalues equal to the mean of the eigenvalues of the sample based estimate of the covariance matrix), with parameter r:
where I is the nc×nc identity matrix and nc is the dimension of the covariance matrix Σk.
Combining the two methods yields a combined regularization formula:
The discriminant function used in regularized discriminant analysis may be calculated using the new estimate for the class conditional covariance matrices and the quadratic discriminant formula. Regularization parameters of λ=1 and r=0 correspond to a linear discriminant classifier model while λ=0 and r=0 correspond to a quadratic discriminant classifier model. The optimum classifier model may be determined by finding the regularization parameters that yielded the largest value of classification accuracy (lowest value of mean classification error obtained through cross-validation).
To account for differing class proportions, the weighting of the training data for each participant by the (faller and non-faller) class proportions may be implemented. This may be accomplished by setting the prior probability for a given class k equal to the proportion of that recording labeled as class k. Prior to training, features may be normalized to have zero mean and unity standard deviation. These normalizing data may then be applied to, the testing data. Each case in the testing set may then be classified by assigning it to the class with the largest value of the discriminant function.
Feature selection may subsequently be performed, such as through a filter method or a wrapper method. Filter methods rely on general characteristics of the data, such as correlation with class labels, to evaluate and select the feature subsets without involving the classifier algorithm. Wrapper methods use the performance of the classifier on the given dataset to evaluate each candidate feature subset. Wrapper methods may search for a more optimal feature set for a given classifier algorithm. Unlike filter based methods, a wrapper-based method may consider interactions between features and may contain less redundancy. The filter method may use a nearest neighbor criterion to add and remove features from the feature subset. A wrapper method, such as sequential forward feature selection, may sequentially add features to an empty set until the addition of further features does not increase the classification accuracy.
Applying this technique not only produces parameters for a probability estimate of the risk of future falls, but also isolates parameters which may be related to specific function deficits, such as physical and sensory deficits. For example, selection of minimum ground clearance (MGC), or angular velocity parameters related to MGC (e.g., selection of mean absolute-valued vertical angular velocity and acceleration), in the feature set could indicate that the test participant associated with the model had poor core or lower limb strength, which is often related to low MGC values. For example, selection of the MGC or related angular velocity parameters informs a clinician of what aspects of a participant's movement places that person at risk of falls. The diagnosis may be tailored to an individual participant, and allows for tailored intervention or treatment to prevent future falls.
An optimum classifier model may be developed for subsets of the data. For example, a first classifier model may be developed for all male test participants. A second classifier model may be developed for all female test participants under the age of 75, and a third classifier model may be developed for all female test participants over the age of 75.
After the data is classified by gender and age, a grid search may be carried out to determine optimum feature set (using each feature selection method) and model parameters (λ and r) for each of the models. For example, when the data set is stratified (e.g., into data taken from male participants, data from female participants under 75 years of age, and data from female participants over 75 years of age) the optimum feature set and model parameters may be determined for each of the three models. This operation may attempt to determine the optimum classifier configuration in terms of features and classifier model parameters employed in each of the models for both methods of feature selection.
The process above generates optimum features and classifier model parameters. Table IIA lists example optimum parameters that were determined using a retrospective approach. Table IIB lists example optimum parameters that were determined using a prospective approach.
The performance of the algorithm may be estimated using cross validation. For example, the data may be randomly split into 10 equal sections or folds. Nine of the folds may be used to train the classifier and the remaining fold may then be used to test the performance of the classifier. Repeating this procedure 10 times and taking the mean results in an unbiased, low variance estimate of the classifier's performance.
Metrics for the accuracy of the classifier includes the accuracy (Acc), Sensitivity (Sens) and specificity (Spec). Sensitivity can be defined as the proportion of fallers (as labeled by the geriatrician evaluating the subject in the clinic) correctly identified by the model. Similarly, specificity can be defined as the proportion of non-fallers that are correctly identified by the model. Accuracy can then be defined as the overall percentage of patients correctly classified. Receiver operating characteristic (ROC) curves may be generated for each classifier model using the test set probability outputs obtained by cross validation. The area under the ROC curve may be used as an index of each statistical model's performance. Table IIIA below shows example accuracy data for classifier models trained using a retrospective approach. There, the method on average correctly classified 81.32% of participants with and without a history of falls. Table IIIB shows example accuracy data for classifier models trained using a prospective approach. There, the method on average correctly classified 79.05% of participants with and without a history of falls.
The top panel in
Turning now to
The illustrated IOH 52, sometimes referred to as a Southbridge of a chipset, functions as a host device and communicates with the network controller 54, which could provide off-platform communication functionality for a wide variety of purposes such as cellular telephone (e.g., W-CDMA (UMTS), CDMA2000 (IS-856/IS-2000), etc.), WiFi (e.g., IEEE 802.11, 1999 Edition, LAN/MAN Wireless LANS), Low-Rate Wireless PAN (e.g., IEEE 802.15.4-2006, LR-WPAN), Bluetooth (e.g., IEEE 802.15.1-2005, Wireless Personal Area Networks), WiMax (e.g., IEEE 802.16-2004, LAN/MAN Broadband Wireless LANS), Global Positioning System (GPS), spread spectrum (e.g., 900 MHz), and other radio frequency (RF) telephony purposes. In the illustrated example, the network controller 54 obtains angular velocity data 62 wirelessly (e.g., from a data aggregator over a Bluetooth connection), and provides the angular velocity data 62 to the processor 48 for further analysis. The illustrated processor 48 calculates TUG parameters 44 (
The other controllers 56 could communicate with the IOH 52 to provide support for user interface devices such as a display, keypad, mouse, etc. in order to allow a user to interact with and perceive information from the system 46.
Embodiments of the present invention are applicable for use with all types of semiconductor integrated circuit (“IC”) chips. Examples of these IC chips include but are not limited to processors, controllers, chipset components, programmable logic arrays (PLA), memory chips, network chips, and the like. In addition, in some of the drawings, signal conductor lines are represented with lines. Some may be thicker, to indicate more constituent signal paths, have a number label, to indicate a number of constituent signal paths, and/or have arrows at one or more ends, to indicate primary information flow direction. This, however, should not be construed in a limiting manner. Rather, such added detail may be used in connection with one or more exemplary embodiments to facilitate easier understanding of a circuit. Any represented signal lines, whether or not having additional information, may actually comprise one or more signals that may travel in multiple directions and may be implemented with any suitable type of signal scheme, e.g., digital or analog lines implemented with differential pairs, optical fiber lines, and/or single-ended lines.
Example sizes/models/values/ranges may have been given, although embodiments of the present invention are not limited to the same. As manufacturing techniques (e.g., photolithography) mature over time, it is expected that devices of smaller size could be manufactured. In addition, well known power/ground connections to IC chips and other components may or may not be shown within the figures, for simplicity of illustration and discussion, and so as not to obscure certain aspects of the embodiments of the invention. Further, arrangements may be shown in block diagram form in order to avoid obscuring embodiments of the invention, and also in view of the fact that specifics with respect to implementation of such block diagram arrangements are highly dependent upon the platform within which the embodiment is to be implemented, i.e., such specifics should be well within purview of one skilled in the art. Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that embodiments of the invention can be practiced without, or with variation of, these specific details. The description is thus to be regarded as illustrative instead of limiting.
The term “coupled” is used herein to refer to any type of relationship, direct or indirect, between the components in question, and may apply to electrical, mechanical, fluid, optical, electromagnetic, electromechanical or other connections. In addition, the terms “first”, “second”, etc. are used herein only to facilitate discussion, and carry no particular temporal or chronological significance unless otherwise indicated.
Those skilled in the art will appreciate from the foregoing description that the broad techniques of the embodiments of the present invention can be implemented in a variety of forms. Therefore, while the embodiments of this invention have been described in connection with particular examples thereof, the true scope of the embodiments of the invention should not be so limited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and following claims.
