Locomotion Prediction for Lower Limb Prostheses in Complex Environments via sEMG and Inertial Sensors

Fang Peng,Hang Su,Cheng Zhang,Jiehao Li,Bugong Xu,Zhen Wang,Rongxin Cui

doi:10.1155/2020/8810663

Abstract

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of a completed gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic (sEMG) and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis.

Highlights

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait
Young et al [20] compared the contribution of surface electromyographic (sEMG) sensors and mechanical sensors embedded on powered prostheses and found that the recognition accuracy obtained by sEMG and inertial sensors (a six-axis inertial measurement unit (IMU) located on the shank) was significantly higher than that of other sensors. ese results have indicated that multisensor fusion can obviously improve classification accuracy; especially, sEMG sensor and inertial sensor fusion is encouraging [21, 22]. us, the remaining problem is how to utilize the sensors to predict upcoming locomotion modes, which is essential for the powered prosthesis to actuate the artificial joints correctly
We evaluated the performance of four classification algorithms, which include support vector machine (SVM), quadratic discriminant analysis (QDA), light gradient boosting machine (LightGBM), and random forest (RF). e SVM is a machine learning algorithm based on the statistical learning theory

Summary

Introduction

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. In [24], the authors proposed a forward predictor to identify and respond to the user’s intent, built an adaptive sEMG model, and added the label of backward estimation into forwarding predictor, and they extracted a 300 ms window of data before a gait event for forward prediction and used DBN and linear discriminant analysis (LDA) as forward and backward classifiers, respectively. These intention recognition methods mainly focused on the recognition of the performed gait or the transitional movement had been occurred. If an amputee is walking slowly or intermittently, it is not accurate to predict the step based entirely on the prior gait

Methods

Results

Conclusion