TriboMotion: A Self-Powered Triboelectric Motion Sensor in Wearable Internet of Things for Human Activity Recognition and Energy Harvesting

Human physical activity recognition is widely used in medical diagnosis, well-being management, and rehabilitation treatment. In spite of various Internet of Things (IoT) designs available in the literature, power resources often limit the lifetime of IoT. Regarding this weakness, this paper develops a new motion sensor system in wearable IoT (WIoT) for human physical activity recognition without any signal conditioning circuits. The triboelectricity-based physical model is explored in designing the motion sensor. It enables to collect motion signals caused by physical activities without any power supply. In addition, the triboelectric structure can be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion and a relatively stable voltage when involving continuous activities. Such a new design lays the foundations for constructing the next generation self-powered WIoT systems. Our new design has been extensively evaluated, where most common activities including sitting and standing, walking, climbing upstairs and downstairs, and running are used. The experimental results demonstrate that our system can achieve similar comparable performance as the state of the art for physical activity recognition at an average successful accuracy of over 80%. At the same time, our system reduces more than 25% energy consumption of the entire sensing hardware system which includes the sensor, microcontroller, and corresponding circuits.