| Summary: | A complete microcomputer system is described, GesRec3D, which facilitates the data acquisition, segmentation, learning, and recognition of 3-Dimensional arm gestures, with application as a Augmentative and Alternative Communication (AAC) aid for people with motor and speech disability. The gesture data is acquired from a Polhemus electro-magnetic tracker system, with sensors attached to the finger, wrist and elbow of one arm. Coded gestures are linked to user-defined text, to be spoken by a text-to-speech engine that is integrated into the system. A segmentation method and an algorithm for classification are presented that includes acceptance/rejection thresholds based on intra-class and inter-class dissimilarity measures. Results of recognition hits, confusion misses and rejection misses are given for two experiments, involving predefined and arbitrary 3D gestures.
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