title: Hand gesture recognition based on surface electromyography.
abstract: Human hands are the most dexterous of human limbs and hand gestures play an important role in non-verbal communication. Underlying electromyograms associated with hand gestures provide a wealth of information based on which varying hand gestures can be recognized. This paper develops an inter-individual hand gesture recognition model based on Hidden Markov models that receives surface electromyography (sEMG) signals as inputs and predicts a corresponding hand gesture. The developed recognition model is tested with a dataset of 10 various hand gestures performed by 25 subjects in a leave-one-subject-out cross validation and an inter-individual recognition rate of 79% was achieved. The promising recognition rate demonstrates the efficacy of the proposed approach for discriminating between gesture-specific sEMG signals and could inform the design of sEMG-controlled prostheses and assistive devices.
authors.forename: Ali-Akbar
authors.initials: AA
authors.lastname: Samadani
authors.forename: Dana
authors.initials: D
authors.lastname: Kulic
mesh_terms: D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D005868:Gestures; D006225:Hand; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D033281:Nontherapeutic Human Experimentation; D009633:Nonverbal Communication; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results