BackHand:Sensing Hand Gesture via Back of the Hand
Date Issued
2016
Date
2016
Author(s)
Lin, Jhe-Wei
Abstract
In this paper, we explore using the back of hands for sensing hand ges- tures, which interferes less than glove-based approaches and provides better recognition than sensing at wrists and forearms. Our prototype, BackHand, uses an array of strain gauge sensors affixed to the back of hands, and applies machine learning techniques to recognize a variety of hand gestures. We conducted a user study with 10 participants to better understand ges- ture recognition accuracy and the effects of sensing locations. Results showed that sensor reading patterns differ significantly across users, but are consis- tent for the same user. The leave-one-user-out accuracy is low at an average of 27.4%, but reaches 95.8% average accuracy for 16 popular hand gestures when personalized for each participant. The most promising location spans the 1/8˷1/4 area between the metacarpophalangeal joints (MCP, the knuckles between the hand and fingers) and the head of ulna (tip of the wrist).
Subjects
Gesture recognition
wearable interface
back of the hand
hand gesture interface
strain gauge
machine learning
gestural interaction
Type
thesis
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