Abstract
Hand gestures will become a mainstream method of manipulating human computer interfaces (HCIs). For disabled people with mobility problems, hand gesture-based HCIs should be specifically designed. To achieve effective hand gesture HCIs, this study integrated a mobile service robot platform, three-dimensional (3D) imaging sensors, and wearable Myo armband device. Four kernel techniques are presented: (1) Myo armband software development kit hand gesture recognition using a two-layer hierarchy scheme to significantly increase hand gesture command numbers, (2) identity recognition of users using clustering-based support vector machine classifiers with a designed root mean square surface electromyography (RMS-sEMG) feature, (3) robot vehicle navigation with effective obstacle avoidance using a conceptually simple and computationally fast approach, and (4) efficient vehicle positioning based on the face-detection information of the user provided from the 3D imaging sensor to receive the hand gestures commands of the user with disabilities.
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