Personalized Gesture Classification for Encouraging Non-Sedentary Behavior During Technology Use in People with Motor Disabilities
Affiliation Type:
Academia
Keywords:
Electromyography, Classification, Gestures, Motor Disability
Abstract:
We developed a personalized electromyography gesture classifier to encourage non-sedentary behavior during technology use. Sedentary behavior is associated with negative health outcomes, and individuals with disabilities are twice as likely to be sedentary compared to the general population. Our classifier had 74% accuracy for 10 gesture classes across 25 participants with upper-body motor disabilities.
Track ID:
2.12
Track Name:
Technology for Women & Children’s Health/Equity and Access for Well-health 2
