Comparison of kinematic and pressure measurement reference methods used in gait event detection

Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals

As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man–machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.

Understanding responses to gait instability from plantar pressure measurement and the relationship to balance and mobility in lower-limb amputees

BACKGROUND

Measuring responses to a more unstable walking environment at the point-of-care may reveal clinically relevant strategies, particularly for rehabilitation. This study determined if temporal measures, center of pressure-derived measures, and force impulse measures can quantify responses to surface instability and correlate with clinical balance and mobility measures.

METHODS

Thirty-one unilateral amputees, 11 transfemoral and 20 transtibial, walked on level and soft ground while wearing pressure-sensing insoles. Foot-strike and foot-off center of pressure, center of pressure path, temporal, and force impulse variables were derived from F-Scan pressure-sensing insoles.

FINDINGS

Significant differences (P<0.05) between level and soft ground were found for temporal and center of pressure path measures. Twenty regression models (R(2) ≤ 0.840), which related plantar-pressure-derived measures with clinical scores, consisted of nine variables. Stride time was in eight models; posterior deviations per stride in six models; mean CoP path velocity in five models; and anterior-posterior center of pressure path coefficient of variation, percent double-support time, and percent stance in four models.

INTERPRETATION

Center of pressure-derived parameters, particularly temporal and center of pressure path measures, can differentiate between level and soft ground walking for transfemoral and transtibial amputees. Center of pressure-derived parameters correlated with clinical measures of mobility and balance, explaining up to 84.0% of the variability. The number of posterior deviations per stride, mean CoP path velocity stride time, anterior-posterior center of pressure path coefficient of variation, percent double-support time, and percent stance were frequently related to clinical balance and mobility measures.