Flexible Strain Sensor Based on Conductive Hydrogel/KC@PDMS for Neck Motion Control Wheelchair Using EMD-LSTM Algorithm

Many disabled people who are not suitable for the control of the current mainstream manual joystick-controlled wheelchair, so the study of new control methods can provide convenience for these people, including bioelectrical signal control, voice control, visual control, etc., but these control meth...

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Vydané v:IEEE sensors journal Ročník 24; číslo 3; s. 1
Hlavní autori: Wang, Xiaoming, Xiong, Longbo, Dong, Wentao, Guo, Zhilong, Huang, Yifeng, Yao, Daojin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Background noise
Bioelectricity
Carrageenan
Circuit design
Control equipment
Control methods
Field tests
Flexible components
Flexible sensor
Human motion
Mechanical properties
Motion control
Neck
neck movement recognition
Permeability
Polydimethylsiloxane
Sensors
Signal classification
Signal processing
signal processing algorithm
Visual control
Voice control
wheelchair control
Wheelchairs
ISSN:1530-437X, 1558-1748
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Shrnutí:Many disabled people who are not suitable for the control of the current mainstream manual joystick-controlled wheelchair, so the study of new control methods can provide convenience for these people, including bioelectrical signal control, voice control, visual control, etc., but these control methods have peripheral equipment complex or susceptible to environmental noise interference, in contrast, These problems can be solved effectively by using a wearable flexible sensor to recognize human movement signal. To collect and identify the strain signal characteristics of the moving neck muscles, based on the principle of flexible piezoresistive sensor, we designed and produced a K-Carrageenan @PDMS flexible sensor, and designed a series of experiments to explore the conductive properties, mechanical properties, air permeability of the sensor, and also designed the peripheral acquisition circuit and signal processing algorithm. Finally, the EMD-LSTM algorithm is used to classify the signal and explore the feasibility of fitting the signal to the human neck to control the wheelchair. The results show that the maximum tensile rate of the sensor designed by us is as high as 180%, and the air permeability is also improved slightly. After 1000 cycles of stretching, the sensor still has good performance and can effectively collect human neck action signals. Through the field test, the accuracy of this system is up to 95%, which provides a new idea for wheelchair control.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3343358