A Triboelectric Gait Sensor System for Human Activity Recognition and User Identification

Li, Jiarong; Xie, Zhenrong; Wang, Zihan; Lin, Zhien; Lu, Chengyue; Zhao, Zihao; Jin, Yuchao; Yin, Jihong; Mu, Shilong; Zhang, Chaobo; Gui, Weihua; Liang, Xiaojun; Wang, Jiyu; Ding, Wenbo

doi:10.2139/ssrn.4378311

Cited by 1 publication

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“…One significant research direction in this field is gait analysis, which involves attaching sensors to capture human gait information. 47,57,58 By utilizing data analysis methods such as ML, signals from TENG-based socks and insoles can be analyzed for gait monitoring, 13,42,[59][60][61][62][63][64][65][66][67][68][69][70] particularly for recognizing abnormal gaits (as shown in Table S1). There remain continuous challenges to balance the relationship between sensor amount, monitoring accuracy, and comfort for gait detection, that is, designing a minimum system with better sensory performance.…”

Section: Introductionmentioning

confidence: 99%

AIoT‐enhanced health management system using soft and stretchable triboelectric sensors for human behavior monitoring

Xu,

Zhong,

Yue

et al. 2024

EcoMat

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Sedentary, inadequate sleep and exercise can affect human health. Artificial intelligence (AI) and Internet of Things (IoT) create the Artificial Intelligence of Things (AIoT), providing the possibility to solve these problems. This paper presents a novel approach to monitor various human behaviors for AIoT‐based health management using triboelectric nanogenerator (TENG) sensors. The insole with solely one TENG sensor, creating a most simplified system that utilizes machine learning (ML) for personalized motion monitoring, encompassing identity recognition and gait classification. A cushion with 12 TENG sensors achieves real‐time identity and sitting posture recognition with accuracy rates of 98.86% and 98.40%, respectively, effectively correcting sedentary behavior. Similarly, a smart pillow, equipped with 15 sensory channels, detects head movements during sleep, identifying 8 sleep patterns with 96.25% accuracy. Ultimately, constructing an AIoT‐based health management system to analyze these data, displaying health status through human‐machine interfaces, offers the potential to help individuals maintain good health.image

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