Feature Selection and Estimation of Route and Gait During Walking with Route and Speed Changes by Surface Electromyograph Using Transformer

Abstract: Recently, with the increase in the number of elderly people requiring nursing care, walking assist devices using an inverted pendulum model and electrical stimulation have been researched and developed to improve the walking ability of elderly people. However, most of these assistive devices are designed only for straight walking on a flat road and do not support unsteady walking, such as turning and acceleration/deceleration. It has been reported that unsteady walking accounts for 40% of our daily life. Hence, it is important to provide training and support for unsteady walking. The purpose of this study is to develop a walking training and support system for gait that includes unsteady walking. Therefore, we built a model that predicts the gait and route of the next walking cycle by extracting muscle movements from surface electromyography (sEMG) data using machine learning. We used transformer for machine learning because of its powerful expressive capabilities and its success in various fields, such as natural language processing, computer vision, and speech processing. Transformer can also evaluate sEMG features in learning by visualizing attention. sEMG and joint angles were recorded while walking along the walking routes set up in this study, and these data were divided into data sets for each cycle to predict the next walking motion, which were then used for training. sEMG was recorded using a wireless electromyograph and the joint angles were recorded using optical motion capture. A Butterworth bandpass filter was applied to the recorded sEMG to remove noise in the range of 20–450 Hz. Then, sEMG at the maximum voluntary contraction was normalized to the reference. The recorded joint angle data were normalized with respect to the range of motion of the joint. The training model utilizes the encoder block of transformer, which consists of a multi-head attention and feed forward network. The multi-head attention can extract different expressions by setting up multiple attentions in parallel. The learning parameters were Adam for the optimization algorithm, a learning rate of 0.001, a batch size of 1, and the number of epochs was set to the value that optimizes the loss, with the maximum value set to be 200. Therefore, the mean absolute error of the joint angles was 2.04°, which was sufficient to estimate the gait in the next cycle. The visualization of attention confirmed the extraction of different representations, indicating that the learning process was successful.

Keywords: gait analysis, unsteady walk, machine learning, Transformer

DOI: 10.54941/ahfe1004678

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