| Abstract |
| This study aimed to develop a soft fabric-based elbow-bending angle sensor that can replace conventional hard-type inertial sensors and a system for estimating bending angles using it. To enhance comfort during exercise, this study treated four fabrics (Bergamo, E-band, span cushion, and polyester) by single-walled carbon nanotube dip coating to create conductive textiles. Subsequently, one fabric was selected based on performance evaluations, and an elbow flexion angle sensor was fabricated. Gauge factor, hysteresis, and sensing range were employed as performance evaluation metrics. The data obtained using the fabricated sensor showed different trends in sensor values for the changes in the angle during bending and extending movements. Because of this divergence, the two movements were separated, and this constituted the one-step process. In the two-step process, multilayer perceptron (MLP) was employed to handle the complex nonlinear relationships and achieve high data accuracy. Based on the results of this study, we anticipate effective utilization in various smart wearable and healthcare domains. Consequently, a soft- fabric bending angle sensor was developed, and using MLP, nonlinear relationships can be addressed, enabling angle estimation. Based on the results of this study, we anticipate the effective utilization of the developed system in smart wearables and healthcare. |
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| Key Words |
| Bending Angle Textile Sensors, Smart Wearable Healtcare, CNT Dip-Coating, Multilayer Perceptron, 2-Steps Nonlinear Regression, 굽힘 각 텍스타일 센서, 스마트 웨어러블 헬스케어, CNT 함침, 다층 퍼셉트론, 2단계 비선형 회귀 |
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