Adasense: Adaptive Low-Power Sensing And Activity Recognition For Wearable Devices
PROCEEDINGS OF THE 2020 57TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC)(2020)
摘要
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.
更多查看译文
关键词
Human Activity Recognition, Wearable Devices, Low-Power Sensing, IoT, Approximate Computing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络