Exploration of Force Myography and surface Electromyography in hand gesture classification.
Whereas pressure sensors increasingly have received attention as a non-invasive interface for hand gesture recognition, their performance has not been comprehensively evaluated. This work examined the performance of hand gesture classification using Force Myography (FMG) and surface Electromyography (sEMG) technologies by performing 3 sets of 48 hand gestures using a prototyped FMG band and an array of commercial sEMG sensors worn both on the wrist and forearm simultaneously. The results show that the FMG band achieved classification accuracies as good as the high quality, commercially available, sEMG system on both wrist and forearm positions; specifically, by only using 8 Force Sensitive Resisters (FSRs), the FMG band achieved accuracies of 91.2% and 83.5% in classifying the 48 hand gestures in cross-validation and cross-trial evaluations, which were higher than those of sEMG (84.6% and 79.1%). By using all 16 FSRs on the band, our device achieved high accuracies of 96.7% and 89.4% in cross-validation and cross-trial evaluations.
School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Xianta
X
Jiang
School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Lukas-Karim
LK
Merhi
School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
Zhen Gang
ZG
Xiao
School of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. Electronic address: cmenon@sfu.ca.
Carlo
C
Menon
D000328:Adult; D001696:Biomechanical Phenomena; D004576:Electromyography; D005260:Female; D005868:Gestures; D006225:Hand / Q000502:physiology*; D018737:Hand Strength; D006801:Humans; D008297:Male; D055595:Mechanical Phenomena; D012815:Signal Processing, Computer-Assisted
Electromyography; Force Myography; Hand gesture recognition; Machine learning; Wearable sensors