부산대학교 도서관

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.