A flexible, stretchable and triboelectric smart sensor based on graphene oxide and polyacrylamide hydrogel for high precision gait recognition in Parkinsonian and hemiplegic patients. (15th December 2022)

• Record Type: Journal Article
• Authors: Wang, Ziying; Bu, Miaomiao; Xiu, Kunhao; Sun, Jingyao; Hu, Ning; Zhao, Libin; Gao, Lingxiao; Kong, Fanzhong; Zhu, Hao; Song, Jungil; Lau, Denvid
• Is Part Of: Nano energy. Volume 104(2022)Part B
• Publication Date: 2022-12-15
• Subjects: Graphene oxide -- Polyacrylamide -- Hydrogel -- Gait recognition -- Artificial Neural Network -- Wearable; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2022.107978
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: Intelligent gait recognition system plays an important role in the field of identity recognition, physical training and medical diagnostics. In clinical medicine, no definitive diagnostic tool has been developed for the diagnosis of Parkinson's disease and hemiplegia. Thus, there is an urgent need to develop an effective and portable human-machine interaction system to monitor and recognize these symptoms. Herein, a self-powered strain sensor based on graphene oxide-polyacrylamide (GO-PAM) hydrogels is reported to monitor subtle human motions, including gait movements. The sensor can be used as a triboelectric nanogenerator (TENG) to collect mechanical energy. The output power of the TENG based on the 0.02 wt% GO-PAM hydrogel was up to 26 mW, which was 2.2 times that of the pure PAM hydrogel film. The capability of the TENG in powering electrical devices was demonstrated by lighting up 353 light-emitting diodes (LEDs) and powering an electronic thermometer. Besides, a wearable in-shoe monitoring system was designed which includes a flexible insole, a data processing module and a PC interface developed using Python. Among the models with different algorithms, the system with the artificial neural network (ANN) exhibits the highest recognition accuracy of 99.5 % and 98.2 % for human daily-life gait and pathological gait, respectively. This system provides a more convenient option for human gait monitoring and recognition, which can be used for a wide range of medical applications such as early diagnosis, rehabilitation evaluation and treatment of patients. Graphical Abstract: A self-powered strain sensor based on GO-PAM hydrogel was reported to monitor subtle human motion, including gait movements. We improved output electrical performance by doping of GO (the output power of 0.02 wt% GO-PAM hydrogel based TENG was up to 26 mW, which was 2.2 times that of the pure PAM hydrogel film). Besides, we designed a wearable in-shoe monitoring system that includes a flexible insole, data processing module and PC interface developed by Python. Compared to some other gait analysis algorithms those reported in the literature, the recognition accuracy this result is the higher. This system provides a more convenient choice for human gait monitoring and recognition, and can be widely used in early diagnosis, rehabilitation evaluation and treatment of patients and other medical fields. ga1 Highlights: Graphene oxide and polyacrylamide are used to be a triboelectric nanogenerator. The excellent output electrical performance was obtained by doping of GO in GO-PAM. The recognition accuracy of artificial neural network (ANN) is 99.5 % for human gait. This system paves the way to smart early diagnosis and rehabilitation evaluation.