Hand Gesture Recognition Using Multiple Acoustic Measurements at Wrist

This article investigates the use of acoustic signals recorded at the human wrist for hand gesture recognition. The prototype consists of 40 microphones to be worn at the wrist. The gesture recognition performance is evaluated through the identification of 36 gestures in American sign language (ASL), including 26 ASL alphabetical characters and 10 ASL numbers. The optimal area for sensor band placement (distal/proximal) is examined to reveal the location of the highest discrimination accuracy. Ten subjects are recruited to perform over ten trials for each set of hand gestures. Using mutual information-based feature selection methods, two time-domain features: difference absolute mean value and log-energy entropy, is selected from 35 commonly used time-domain features for the hand gesture classification. As a proof-of-concept, two common classification methods, linear discriminant analysis and support vector machine, is used to classify hand gestures from the acoustic measurements recorded by each subject. Results show the intrasubject average classification accuracy above 90% using the two features with all 40 microphones, while the average classification accuracy exceeding 84% is obtained using ten microphones. These results indicate that acoustic signatures from the human wrist can be utilized for hand gesture recognition, while the use of few, simple features, with low computational requirements is sufficient to characterize some hand gestures. The proposed technique demonstrates a promising means for developing a low-cost wearable hand gesture recognition device using microphones.