We describe our initial efforts to learn high-level human behaviors from low-level gestures observed using on-body sensors. Such an activity discovery system could be used to index captured journals of a person's life automatically. In a medical context, an annotated journal could assist therapists in helping to describe and treat symptoms characteristic to behavioral syndromes such as autism. We review our current work on user-independent activity recognition from continuous data where we identify "interesting" user gestures through a combination of acceleration and audio sensors placed on the user's wrists and elbows. We examine an algorithm that can take advantage of such a sensor framework to automatically discover and label recurring behaviors, and we suggest future work where correlations of these low-level gestures may indicate higher-level activities
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