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arXiv · 2006.05884

AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices

Marina Neseem·Jon Nelson·Sherief Reda
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Abstract

Wearable devices have strict power and memory limitations. As a result, there is a need to optimize the power consumption on those devices without sacrificing the accuracy. This paper presents AdaSense: a sensing, feature extraction and classification co-optimized framework for Human Activity Recognition. The proposed techniques reduce the power consumption by dynamically switching among different sensor configurations as a function of the user activity. The framework selects configurations that represent the pareto-frontier of the accuracy and energy trade-off. AdaSense also uses low-overhead processing and classification methodologies. The introduced approach achieves 69% reduction in the power consumption of the sensor with less than 1.5% decrease in the activity recognition accuracy.

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Marina Neseem, Jon Nelson, Sherief Reda. 2020-06-10. AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices. https://arxiv.org/abs/2006.05884

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