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

Abstract

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.

A Determination Method for Gait Event Based on Acceleration Sensors

“…Gait locomotion, as a fundamental activity for all humans, is a cyclic spatiotemporal complex act. Gait information can be acquired by collecting kinematics signals, bioelectrical signals, videos and images [7][8][9][10][11][12][13]. In traditional gait analysis methods, a three-dimensional motion capture (3D Mo-Cap) system and force plate pressure signal can accurately describe the 3D motion of the human body, while can accurately detect the gait event with error of 0.13% [14], which is often called the golden standard method in gait analysis [15][16][17][18].…”

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

“…This indicated that using the greatest number of features lead to more accurate predictions. A backpropagation neural network (BPNN) is implemented by Zhang et al [76] to detect five phases of gait. The authors evaluated the effect of variations in the load carried by the users on the EMG activity, and the ability of an algorithm trained on data from users carrying one load level to perform accurate predictions when tested on data from users carrying multi-load levels.…”

“…Its accuracy ranged between 69% and 76% while its F-score between 70% and 77%. A KNN is implemented by Zhang et al [76], for the detection of five gait phases. The KNN algorithm had a lower performance than BPNN.…”

Investigation of EMG parameter for transtibial prosthetic user with flexion and extension of the knee and normal walking gait: A preliminary study

“…Electromyography (EMG) is one of the manners used in gait analysis, many different activities and an assortment of implementations, including EMG for upper and lower limb prostheses. 5–12 According to lower-limb amputees, prosthetics, including EMG within the socket, can provide a method to predict the user’s movement. Based on this level of continuous sensing, it gives us a continuous signal, which is sent to the controller to give the necessary command for the required movement.…”