Wearable Recognition System for Complex Motions Based on Hybrid Deep-Learning-Enhanced Strain Sensors

Abstract

Wearable recognition systems based on flexible electronics present immense potential for applications in human–machine interfaces, medical care, soft robots, etc. However, they experience challenges in terms of the nonideal consistency and stability of flexible sensors, which are responsible for detecting physical signals from human motions. These challenges hinder the improvement of recognition precision and capability in the wearable systems. Furthermore, the computational consumption for the recognition increases as more sensors are used to extensively gather information for distinguishing between complex motions. Herein, a wearable recognition system based on deep-learning-enhanced strain sensors for distinguishing between the complex motions of the human body is presented. A strain sensor based on peak–valley microstructures is fabricated and packaged to improve consistency and stability. Moreover, a lightweight hybrid convolutional neural network long short-term memory model is designed to lower the computational costs of the deep learning process. In particular, by designing Butterworth filtering and Z-score normalization algorithms, the error in feature extraction caused by sensor signal fluctuation is reduced, thereby improving the recognition accuracy of the proposed wearable system to 95.72% for seven gait motions and 100% for four different continuous series of Tai Chi forms.