Affiliation:
1. CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro‑Nano Energy and Sensor Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China
2. School of Nanoscience and Technology University of Chinese Academy of Sciences Beijing 100049 P. R. China
3. College of Materials Science and Engineering Key Laboratory of Material Processing and Mold (Ministry of Education) Henan Key Laboratory of Advanced Nylon Materials and Application Zhengzhou University Zhengzhou 450001 P. R. China
4. Department of Software Engineering Harbin University of Science and Technology Rongcheng 264300 P. R. China
5. School of Material Science and Engineering Georgia Institute of Technology Atlanta GA 30332‑0245 USA
Abstract
AbstractGait analysis provides a convenient strategy for the diagnosis and rehabilitation assessment of diseases of skeletal, muscular, and neurological systems. However, challenges remain in current gait recognition methods due to the drawbacks of complex systems, high cost, affecting natural gait, and one‐size‐fits‐all model. Here, a highly integrated gait recognition system composed of a self‐powered multi‐point body motion sensing network (SMN) based on full textile structure is demonstrated. By combining of newly developed energy harvesting technology of triboelectric nanogenerator (TENG) and traditional textile manufacturing process, SMN not only ensures high pressure response sensitivity up to 1.5 V kPa−1, but also is endowed with several good properties, such as full flexibility, excellent breathability (165 mm s−1), and good moisture permeability (318 g m−2 h−1). By using machine learning to analyze periodic signals and dynamic parameters of limbs swing, the gait recognition system exhibits a high accuracy of 96.7% of five pathological gaits. In addition, a customizable auxiliary rehabilitation exercise system that monitors the extent of the patient's rehabilitation exercise is developed to observe the patient's condition and instruct timely recovery training. The machine learning‐assisted SMN can provide a feasible solution for disease diagnosis and personalized rehabilitation of the patients.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Subject
Electrochemistry,Condensed Matter Physics,Biomaterials,Electronic, Optical and Magnetic Materials
Cited by
23 articles.
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