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

• Published in: Advanced functional materials 2024-10, Vol.34 (42), p.n/a
• Main Authors: Liu, Chao, Gu, Rui, Yang, Jiahong, Luo, Lin, Chen, Mingxia, Xiong, Yao, Huo, Ziwei, Liu, Yang, Zhang, Keteng, Gong, Jie, Wei, Liang, Lei, Yanqiang, Wang, Zhong Lin, Sun, Qijun
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; biomechanical energy harvesting; Digital twins; Health care; Internet of Things; Knee; Machine learning; Optimization techniques; self‐powered; smart healthcare; Three dimensional printing; Virtual reality; wearable sensors
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202405104

In 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. A self‐powered, bidirectional knee joint motion monitoring system is developed using a dual‐ratchet device fabricated via 3D printing technology. This innovative prototype can harness knee joint movements to power electronic devices and enables precise real‐time knee monitoring via virtual reality and digital twin technology, achieving up to 97% accuracy in knee movement detection.