Self‐Powered Biomimetic Pressure Sensor Based on Mn–Ag Electrochemical Reaction for Monitoring Rehabilitation Training of Athletes

• Published in: Advanced science Vol. 11; no. 25; pp. e2401515 - n/a
• Main Authors: Yang, Ziyan, Wang, Qingzhou, Yu, Huixin, Xu, Qing, Li, Yuanyue, Cao, Minghui, Dhakal, Rajendra, Li, Yang, Yao, Zhao
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.07.2024; John Wiley and Sons Inc; Wiley
• Subjects: biomimetic pressure sensor; Electrodes; Electrolytes; Graphene; human health monitoring; hydrogel electrolyte; Hydrogels; Microstructure; Monitoring systems; Oxidation; Polyvinyl alcohol; potentiometry; Rehabilitation; Scanning electron microscopy; Sensors; Simulation; Skin; Spectrum analysis; wearable device; wearable device; human health monitoring; hydrogel electrolyte; biomimetic pressure sensor; potentiometry
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202401515

Self‐powered pressure detection using smart wearable devices is the subject of intense research attention, which is intended to address the critical need for prolonged and uninterrupted operations. Current piezoelectric and triboelectric sensors well respond to dynamic stimuli while overlooking static stimuli. This study proposes a dual‐response potentiometric pressure sensor that responds to both dynamic and static stimuli. The proposed sensor utilizes interdigital electrodes with MnO2/carbon/polyvinyl alcohol (PVA) as the cathode and conductive silver paste as the anode. The electrolyte layer incorporates a mixed hydrogel of PVA and phosphoric acid. The optimized interdigital electrodes and sandpaper‐like microstructured surface of the hydrogel electrolyte contribute to enhanced performance by facilitating an increased contact area between the electrolyte and electrodes. The sensor features an open‐circuit voltage of 0.927 V, a short‐circuit current of 6 µA, a higher sensitivity of 14 mV/kPa, and outstanding cycling performance (>5000 cycles). It can accurately recognize letter writing and enable capacitor charging and LED lighting. Additionally, a data acquisition and display system employing the proposed sensor, which facilitates the monitoring of athletes’ rehabilitation training, and machine learning algorithms that effectively guide rehabilitation actions are presented. This study offers novel solutions for the future development of smart wearable devices. A self‐powered pressure sensor utilizing a potential conversion mechanism based on electrochemical reactions is proposed. The proposed sensor incorporates interdigital electrodes and a microstructured electrolyte to increase the sensitive area. Additionally, a data acquisition and machine learning algorithm system are developed using the sensor microcontroller for real‐time monitoring of sports rehabilitation.