Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Coming to grips with the objects we grasp: detecting interactions with efficient wrist-worn sensors
Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction
A compressive sensing scheme of frequency sparse signals for mobile and wearable platforms
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Personal state and emotion monitoring by wearable computing and machine learning
BCS-HCI '11 Proceedings of the 25th BCS Conference on Human-Computer Interaction
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With sensors becoming smaller and more power efficient, wearable sensors that anyone could wear are becoming a feasible concept. We demonstrate a small lightweight module, called Porcupine, which aims at continuously monitoring human activities as long as possible, and as fine-grained as possible. We present initial analysis of a set of abstraction algorithms that combine and process raw accelerometer data and tilt switch states, to get descriptors of the user's motion-based activities. The algorithms are running locally, and the information they produce is stored in on-board memory for later analysis.