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Recognizing mimicked autistic self-stimulatory behaviors using HMMs

Children with autism often exhibit self-stimulatory (or "stimming") behaviors. We present an on-body sensing system for continuous recognition of slimming 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 slimming events can be detected with 100% accuracy by allowing minor frame-level insertion errors.