title: Hierarchical domain adaptation for SEMG signal classification across multiple subjects.
abstract: Large variations in Surface Electromyogram (SEMG) signal across different subjects make the process of automated signal classification as a generalized tool, challenging. In this paper, we propose a domain adaptation methodology that addresses this challenge. In particular we propose a hierarchical sample selection methodology, that selects samples from multiple training subjects, based on their similarity with the target subject at different levels of granularity. We have validated our framework on SEMG data collected from 8 people during a fatiguing exercise. Comprehensive experiments conducted in the paper demonstrate that the proposed method improves the subject independent classification accuracy by 21% to 23% over the cases without domain adaptation methods and by 14% to 20% over the existing state-of-the-art domain adaptation methods.
authors.affiliation: #Center for Cognitive Ubiquitous Computing, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona 85287, USA.
authors.forename: Rita
authors.initials: R
authors.lastname: Chattopadhyay
authors.forename: Narayanan C
authors.initials: NC
authors.lastname: Krishnan
authors.forename: Sethuraman
authors.initials: S
authors.lastname: Panchanathan
mesh_terms: D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D013499:Surface Properties