Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

• Published in: Npj flexible electronics Vol. 4; no. 1; pp. 1 - 12
• Main Authors: Zhang, Zixuan, He, Tianyiyi, Zhu, Minglu, Sun, Zhongda, Shi, Qiongfeng, Zhu, Jianxiong, Dong, Bowei, Yuce, Mehmet Rasit, Lee, Chengkuo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.10.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/301/1005; Artificial intelligence; Chemistry and Materials Science; Cost analysis; Deep learning; Electronics; Electronics and Microelectronics; Energy consumption; Energy harvesting; Gait; Health care; Instrumentation; Internet of Things; Machine learning; Massive data points; Materials Science; Optical and Electronic Materials; Polymer Sciences; Smart buildings; Virtual reality; Wearable technology
• ISSN: 2397-4621; 2397-4621
• DOI: 10.1038/s41528-020-00092-7

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.