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

摘要

In the age of the artificial intelligence of things (AIoT), wearable devices havebeen extensively developed for smart healthcare. This paper proposes aself-powered and self-sensing lower-limb system (SS-LS) with negative energyharvesting and motion capture for smart healthcare. The SS-LS achievesself-sustainability via a half-wave electromagnetic generator (HW-EMG) thatrecovers negative work from walking with a low cost of harvesting.Additionally, the motion capture function of the system is achieved by thethree-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 outputpower of 11.2 mW, sufficient to power a wireless sensor. The three-channelvoltage signal of TC-TENG fits well with the binary signal, which can preciselydetect the angle and direction of rotation. Furthermore, the SS-LSdemonstrates an identification accuracy of 99.68% and a motion detectionaccuracy of 99.96% based on an LSTM deep learning model. Demonstrationsof Parkinson’s disease and fall detection and monitoring of three trainingmodes (sit-and-stand, balance, and walking training) are also performed,which exhibit outstanding sensing capabilities. The SS-LS is highly promisingin sports rehabilitation medicine and can contribute to the development ofsmart healthcare.