Abstract
This paper presents a convolutional neural network-based classification of the hand flexion and extension gestures used in wrist recovery after injury. The hand gesture recognition device used in our study is the Leap Motion controller. The Leap Motion device's inability to accurately differentiate the left hand from the right hand when performing hand rotation gestures was eliminated by introducing hand and thumb direction vectors into the database used to train the neural network. A 3D environment was created for the introduction of the data describing the gesture into the database. A classification accuracy of 95% was achieved for the hand flexion and extension gesture divided into three levels for each hand. The populated database may also be used to classify other gestures involving hand rotation.
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