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Children with autism often exhibit self-stimulatory (or "stimming") behaviors. We present an on-body sensing system for continuous recognition of stimming activity. By creating a system to recognize and monitor stimming behaviors, we hope to provide autism researchers with detailed, quantitative data. In this paper, we compare isolated and continuous recognition rates of emulated autistic stimming behaviors using hidden Markov models (HMMs). We achieved an overall system accuracy 68.57% in continuous recognition tests. However, the occurrence of stimming events can be detected with 100% accuracy by allowing minor frame-level insertion errors.

The Gesture and Activity Recognition Toolit (GART) is a user interface toolkit designed to enable the development of gesturebased applications. GART provides an abstraction to machine learning algorithms suitable for modeling and recognizing different types of gestures. The toolkit also provides support for the data collection and the training process. In this paper, we present GART and its machine learning abstractions. Furthermore, we detail the components of the toolkit and present two example gesture recognition applications.

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