A Triboelectric Nanogenerator‐Based Smart Insole for Multifunctional Gait Monitoring
Accurately monitoring human gait is critical for health evaluation and/or early diagnosis, especially for elder and injured people's healthcare. The presence of gait abnormalities could be important predictors of the risk of developing diseases. Herein, a triboelectric nanogenerator (TENG)‐base...
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Veröffentlicht in: | Advanced materials technologies 2019-02, Vol.4 (2), p.n/a |
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Sprache: | eng |
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Zusammenfassung: | Accurately monitoring human gait is critical for health evaluation and/or early diagnosis, especially for elder and injured people's healthcare. The presence of gait abnormalities could be important predictors of the risk of developing diseases. Herein, a triboelectric nanogenerator (TENG)‐based smart insole for real‐time gait monitoring is reported. Due to the novel air‐pressure‐driven structural design, the elastic TENG‐based sensors exhibit compelling features including simple fabrication, fast response time, high durability, and excellent mechanical robustness. The TENG‐based sensors can be easily integrated into the conventional insole so that it can convert the mechanical triggering/impact into electrical output. By analyzing such electrical signals, the smart insole could accurately monitor and distinguish various gait patterns in real time, including jump, step, walk, and run. The smart insole could also be used to monitor the abnormality of gait for rehabilitation assessment. In addition, the smart insole can play another important role in healthcare applications, for example, serving as a fall‐down alert system for elder people or patients. This work not only paves a new way for real‐time and long‐term gait monitoring, but also presents a new perspective for the practical applications of remote clinical biomotion analysis.
A triboelectric nanogenerator (TENG)‐based smart insole for real‐time gait monitoring is reported. The TENG‐based sensors can be integrated into the conventional insole so that it can convert the mechanical triggering/impact into electrical output. This work not only paves a new way for real‐time gait monitoring, but also presents a new perspective for the practical applications of remote clinical bio‐motion analysis. |
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ISSN: | 2365-709X 2365-709X |
DOI: | 10.1002/admt.201800360 |