학술논문
A 3D-Printed, Adjustable Armband for Electromyography-Based Finger Movement Classification With Haptic Feedback
Document Type
Conference
Author
Wang, Michelle; Bulger, Miasya; Dai, Yue; Noel, Kira; Axon, Christopher; Brandenberger, Anna; Fay, Stephen; Gao, Zenghao; Gilmer, Saskia; Hamdan, Jad; Humane, Prateek; Jiang, Jennifer; Killian, Cole; Langleben, Ian; Li, Bonnie; Zamora, Alejandra Martinez; Mavromatis, Stylianos; Njini, Sasha; Riachi, Roland; Rong, Carrie; Zhen, Andy; Xiong, Marley
Source
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2020 IEEE International Conference on. :3460-3465 Oct, 2020
Subject
Language
ISSN
2577-1655
Abstract
Recent work in prosthetic devices suggests that forearm surface electromyography (sEMG) is a promising technology for human-computer interactions. Specifically, a system able to detect individual finger movement can have many clinical and nonclinical applications. Popular consumer-grade sEMG armbands are limited by their fixed electrode arrangement, which can negatively affect the classification of subtle finger gestures. We propose a low-cost, 3D-printed armband with fully adjustable electrode placement for the detection of single-finger tapping motions. We trained machine learning classifiers on features extracted from eight-channel sEMG signals to detect movement from nine fingers. We obtained a classification accuracy of 71.5 ± 1.1% for a K-Nearest Neighbours (KNN) classifier using features extracted from 500 ms windows of sEMG data. Moreover, a KNN model trained on 200 ms windows from a subset of particularly clean data obtained an accuracy of 93.0 ± 0.5%. We also introduce a novel haptic feedback mechanism to improve user experience when using the armband, and propose an augmented reality typing interface as a potential application of our armband.