The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development

Frontiers in Neuroscience

Neuroscience

Can A. Yucesoy¹  Ramazan Tarık Türksoy²  Ahmet Doğukan Keleş³ 

[1] Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye;Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye;Huawei Turkey R&D Center, Istanbul, Türkiye;Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Türkiye;Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany;

关键词: powered ankle prosthesis;    surface electromyogram (sEMG);    long short-term memory neural network;    feature extraction;    lower limb amputation;   

DOI  :  10.3389/fnins.2023.1158280

 received in 2023-02-03, accepted in 2023-06-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson’s correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (rposition = 0.9099 and rmoment = 0.9707) whereas, PL (rposition = 0.9001, rmoment = 0.9703) and GMax+VM (rposition = 0.9010, rmoment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.

【 授权许可】

Unknown   
Copyright © 2023 Keleş, Türksoy and Yucesoy.