A triboelectric gait sensor system for human activity recognition and user identification

Floor-based sensing systems to monitor human activities and identify users are essential for smart homes and intelligent buildings. A low-cost, easy-to-fabricate, and flexible gait sensor system based on triboelectric nanogenerator (TENG) is presented in this paper, which can transform gait movements, even in the low-frequency form, into electrical impulses without an external power source. To realize this, a TENG-based gait sensor unit with an optimized structure design is proposed to enhance the sensing sensitivity. A sensing insole module is formed by arranging the sensor units according to the foot pressure distribution. The sensor distribution is then explored and improved by comparative studies of gait recognition performance, which increases the recognition efficiency and the possible application in edge computing scenarios. Furthermore, a deep learning network is developed based on long short-term memory (LSTM) and residual units to extract deep features from multichannel time-series gait data to boost recognition performance. Experimental results demonstrate that the proposed gait sensor system can be utilized for human activity recognition and user identification with accuracies of 97.9 % and 99.4 %, respectively. Finally, a gait-sensing-based fitness exercise monitoring system is constructed that can estimate calorie expenditure and distinguish between standard and non-standard fitness activities with an accuracy of 97.2 %. This work can be extended to various application scenarios such as security surveillance, health monitoring, and intelligent control, which provides a new ubiquitous self-powered sensing solution for the Internet of Things (IoT).