Estimation of soleus muscle activation patterns from lower limb kinematics during normal level walking using a deep neural network model

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Abstract
The purpose of this study is to develop a deep neural network model for predicting soleus muscle activation patterns from time-series lower-limb joint angles collected during level ground walking at different speeds and to evaluate the prediction performance. An open dataset was used to obtain full lower-limb kinematics, including pelvis, hip, knee, and ankle joints, and soleus muscle activation patterns from 20 control adults. Long short-term memory (LSTM)-based deep learning models were developed and then evaluated for prediction performance by conducting both the random split cross-validation (CVM1) and the leave-one-subject-out cross-validation (CVM2). For both cross-validation methods, the developed models yielded promising error and regression metrics such as the root mean square error and coefficient of determination. However, the CVM2 demonstrated that the prediction performance can be sensitive to individual datasets. The subject factors, such as age, sex, and walking speed, appear to have a negligible effect on the prediction performance for the CVM1. This study demonstrated the feasibility of the developed models to be a template for a potential tool that quantifies muscle activation patterns from joint angles during level ground walking at different speeds.

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