IMU-Based Motion Mode Recognition in Soft Underwater Exosuit

Abstract

By accurately recognizing the wearer’s motion, the underwater exoskeleton enables more efficient human-machine collaboration and provides enhanced assistance in complex and dynamic underwater environments. In this study, we propose a soft underwater exosuit motion mode recognizer based on a long short-term memory network and convolutional neural networks, referred to as LSTM-CNN. This model is designed to perform two tasks: motion mode classification and state transition label recognition. First, the LSTM network extracts features from the time-series data, followed by further feature extraction and classification using the convolutional and fully connected networks. The recognition of motion modes relies on three IMU sensors placed on the left and right legs and the back of the torso of the soft underwater exosuit. On the dataset containing four classes, including non-assist, breaststroke, flutter kick, and underwater walking, LSTM-CNN achieved an overall accuracy of 99.943±0.006% in motion mode classification and 92.101±0.054% in state transition label recognition. The experimental results indicate that the LSTM-CNN achieves better accuracy and performs optimally across various evaluation metrics compared to the other methods.