2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor: From Design to Intention Recognition

• Published in: IEEE robotics and automation letters Vol. 7; no. 4; pp. 10367 - 10374
• Main Authors: Jeong, Hwayeong, Feng, Jirou, Kim, Jung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Contact pressure; Customization; Cutting wear; Electrodes; Electromyography; Fabrication; Fabrics; Gesture recognition; Hand (anatomy); Intention recognition; Laser beam cutting; Man-machine interfaces; Performance evaluation; Perspiration; Robot sensing systems; sEMG sensor; Sensors; Signal quality; soft sensors and actuators; wearable robotics; Wearable technology; Wires; Wrist
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3190620

The surface electromyography(sEMG) sensor is widely used as a human-machine interface in wearable systems. Although numerous studies have applied compact sEMG systems to wearable devices, these are inconvenient and not suitable for long-term use. Herein, we introduce a 2.5D laser cutting method to accelerate customized sensor fabrication from design to production. The customized textile-based sensor provides high wearing comfort and improves the sensor signal quality through stable contact. We implemented a foam-filled electrode to ensure solid skin-electrode contact even during perspiration and varying pressure conditions, and evaluated its performance experimentally. The sensor-integrated garments one for the leg and one for the arm were fabricated with the proposed design method for further evaluation and application. Consistent sensor performance was demonstrated during squats and running (i.e., perspiration) while wearing the leg sensor. The sensor sleeve for the arm was integrated with intention recognition algorithms for hand gesture recognition. A Convolutional Neural Network (CNN) architecture was employed to classify 28 hand gestures, including finger and wrist motions. The average classification accuracy of five subjects achieved 93.21%, and further increased to 94.34% after perspiration.