A Self-Powered and Self-Sensing Lower-Limb System for Smart Healthcare

被引:24
|
作者
Kong, Lingji [1 ,2 ]
Fang, Zheng [1 ,2 ]
Zhang, Tingsheng [1 ,2 ]
Zhang, Zutao [1 ]
Pan, Yajia [1 ]
Hao, Daning [1 ,2 ]
Chen, Jiangfan [1 ,2 ]
Qi, Lingfei [3 ]
机构
[1] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China
[2] Southwest Jiaotong Univ, Yibin Res Inst, Yibin 64000, Peoples R China
[3] Guizhou Univ, Sch Mech Engn, Guiyang 550025, Guizhou, Peoples R China
基金
中国国家自然科学基金;
关键词
deep learning; lower-limb systems; self-powered sensing; smart healthcare; triboelectric nanogenerators; ENERGY HARVESTER; WALKING; COST; SENSORS;
D O I
10.1002/aenm.202301254
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In the age of the artificial intelligence of things (AIoT), wearable devices have been extensively developed for smart healthcare. This paper proposes a self-powered and self-sensing lower-limb system (SS-LS) with negative energy harvesting and motion capture for smart healthcare. The SS-LS achieves self-sustainability via a half-wave electromagnetic generator (HW-EMG) that recovers negative work from walking with a low cost of harvesting. Additionally, the motion capture function of the system is achieved by the three-channel triboelectric nanogenerator (TC-TENG) based on binary code, which can accurately detect the angle and direction of the knee joint rotation. The bench test experiment indicates that the HW-EMG has an average output power of 11.2 mW, sufficient to power a wireless sensor. The three-channel voltage signal of TC-TENG fits well with the binary signal, which can precisely detect the angle and direction of rotation. Furthermore, the SS-LS demonstrates an identification accuracy of 99.68% and a motion detection accuracy of 99.96% based on an LSTM deep learning model. Demonstrations of Parkinson's disease and fall detection and monitoring of three training modes (sit-and-stand, balance, and walking training) are also performed, which exhibit outstanding sensing capabilities. The SS-LS is highly promising in sports rehabilitation medicine and can contribute to the development of smart healthcare.
引用
收藏
页数:20
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