Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit

Huong Thi Thu Vu; Hoang-Long Cao; Dianbiao Dong; Tom Verstraten; Joost Geeroms; Bram Vanderborght

doi:10.3389/fnbot.2022.923164

Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit

Front Neurorobot. 2022 Nov 29:16:923164. doi: 10.3389/fnbot.2022.923164. eCollection 2022.

Authors

Huong Thi Thu Vu^{1

2}, Hoang-Long Cao^{3

4}, Dianbiao Dong⁵, Tom Verstraten³, Joost Geeroms³, Bram Vanderborght¹

Affiliations

¹ Brubotics, Vrije Universiteit Brussel and imec, Brussels, Belgium.
² Faculty of Electronics Engineering Technology, Hanoi University of Industry, Hanoi, Vietnam.
³ Brubotics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium.
⁴ College of Engineering Technology, Can Tho University, Can Tho, Vietnam.
⁵ School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China.

Abstract

Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.

Keywords: assistive devices; deep learning-artificial neural network (DL-ANN); locomotion mode recognition; lower limb prosthesis; machine learning; transportation mode classification; wearable device and IMU sensor.