A Self‐Powered Dual Ratchet Angle Sensing System for Digital Twins and Smart Healthcare

Author:

Liu Chao12,Gu Rui23,Yang Jiahong23,Luo Lin12,Chen Mingxia24,Xiong Yao23,Huo Ziwei23,Liu Yang23,Zhang Keteng12,Gong Jie24,Wei Liang12,Lei Yanqiang2,Wang Zhong Lin25,Sun Qijun1236ORCID

Affiliation:

1. Center on Nanoenergy Research Institute of Science and Technology for Carbon Peak & Neutrality Key Laboratory of Blue Energy and Systems Integration (Guangxi University) Education Department of Guangxi Zhuang Autonomous Region School of Physical Science & Technology Guangxi University Nanning 530004 P. R. China

2. Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China

3. School of Nanoscience and Technology University of Chinese Academy of Sciences Beijing 100049 P. R. China

4. Center on Nanoenergy Research School of Chemistry and Chemical Engineering Guangxi University Nanning 530004 P. R. China

5. Georgia Institute of Technology Atlanta GA 30332 USA

6. Shandong Zhongke Naneng Energy Technology Co., Ltd Dongying 257061 P. R. China

Abstract

AbstractIn the swiftly progressing landscape of wearable electronics and the Internet of Things (IoTs), there is a burgeoning demand for devices that are lightweight, cost‐effective, and self‐powered. In this study, a self‐powered bidirectional knee joint motion monitoring system is introduced, leveraging a dual ratchet sensing (DRS) system fabricated using 3D printing technology. This approach offers substantial economic and portability benefits. The DRS system is engineered to harness the negative work generated from knee joint movements to power commercial electronic devices, obviating the need for additional metabolic energy from the human body. By synergizing the DRS with virtual reality technology, it becomes feasible to monitor knee joint movements in real‐time with remarkable accuracy, presenting a novel avenue for the integration of digital twin technology. Through the amalgamation of convolutional neural network machine learning algorithms with Bayesian optimization techniques, the DRS system can discern up to 97% of knee joint movements, paving the way for innovative applications in smart rehabilitation and healthcare.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Nova Program

Fundamental Research Funds for the Central Universities

Publisher

Wiley

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