论文标题
Anasense：可穿戴设备的自适应低功率感测和活动识别
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices
论文作者
论文摘要
可穿戴设备具有严格的功率和内存限制。结果，有必要在不牺牲准确性的情况下优化这些设备上的功耗。本文介绍了Adasense：一种感应，提取和分类的人类活动识别框架。提出的技术通过在不同的传感器配置之间动态切换作为用户活动的函数来减少功耗。该框架选择代表准确性和能量折衷的帕累托范围的配置。 Adasense还使用低空处理和分类方法。引入的方法可降低传感器功耗69％，活动识别精度降低1.5％。
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.
