Stretchable Self-Powered TENG Sensor Array for Human-Robot Interaction Based on Conductive Ionic Gels and LSTM Neural Network

The flexible and stretchable self-powered sensors are widely applied to the Internet of Things (IoT), human motion monitoring, and human-robot interaction (HRI), and it reveals more and more attentions due to the flexibility and self-powered function. This article focuses on the stretchable triboelectric nanogenerator (TENG) sensor for human gesture monitoring and HRI application based on conductive ionic gels and machine learning. Stretchable self-powered TENG sensor is fabricated by solution method based on polyacrylamide (PAAM)/NaCl conductive hydrogel and silicone rubber. The self-powered TENG sensor array (TENG-SA) is applied to gesture detection at different parts (wrist, elbow, knee, and neck) of human body successfully without external power supply. Conductive PAAM/NaCl hydrogel could be stretched up to 307%, which satisfies the deformation of the human joints. Intelligent sensing system is designed and developed to control the motion of two-wheeled robot through TENG-SA and long and short time memory (LSTM) neural network via Bluetooth. Experiments have demonstrated that the neck motion signals are collected and recognized by intelligent TENG-SA system with LSTM neural network during the neck rotation process, which is applied to control the motion of two-wheeled robot successfully. HRI based on self-powered TENG sensor will promote the interaction among humans and machines/robots to improve the disabled for rehabilitation, medical monitoring, and human-robot cooperation.

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Publication Details

Journal
IEEE Sensors Journal
Published
2024-09-26
DOI
https://doi.org/10.1109/jsen.2024.3464633
Citations
56
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
10.33

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article

Stretchable Self-Powered TENG Sensor Array for Human-Robot Interaction Based on Conductive Ionic Gels and LSTM Neural Network

Wentao Dong, Xiao Cheng, Kun Liu, Kun Xiong et al.
56 citations
IEEE Sensors Journal
Advanced Sensor and Energy Harvesting Materials
10.33
article

Stretchable Self-Powered TENG Sensor Array for Human-Robot Interaction Based on Conductive Ionic Gels and LSTM Neural Network

Wentao Dong, Xiao Cheng, Kun Liu, Kun Xiong, Bo Huang, Kaiqi Sheng
article en
56 citations

Abstract

The flexible and stretchable self-powered sensors are widely applied to the Internet of Things (IoT), human motion monitoring, and human-robot interaction (HRI), and it reveals more and more attentions due to the flexibility and self-powered function. This article focuses on the stretchable triboelectric nanogenerator (TENG) sensor for human gesture monitoring and HRI application based on conductive ionic gels and machine learning. Stretchable self-powered TENG sensor is fabricated by solution method based on polyacrylamide (PAAM)/NaCl conductive hydrogel and silicone rubber. The self-powered TENG sensor array (TENG-SA) is applied to gesture detection at different parts (wrist, elbow, knee, and neck) of human body successfully without external power supply. Conductive PAAM/NaCl hydrogel could be stretched up to 307%, which satisfies the deformation of the human joints. Intelligent sensing system is designed and developed to control the motion of two-wheeled robot through TENG-SA and long and short time memory (LSTM) neural network via Bluetooth. Experiments have demonstrated that the neck motion signals are collected and recognized by intelligent TENG-SA system with LSTM neural network during the neck rotation process, which is applied to control the motion of two-wheeled robot successfully. HRI based on self-powered TENG sensor will promote the interaction among humans and machines/robots to improve the disabled for rehabilitation, medical monitoring, and human-robot cooperation.

IEEE Sensors JournalVol. 24(22)
East China Jiaotong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangxi Province
Affordable and clean energy
Openalex Percentile: Top 1%
Advanced Sensor and Energy Harvesting Materials
10.33
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