Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection
Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection
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DOI：
10.3390/s20010310
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Cook, Diane J.
中科院分区：
文献类型：
--
作者：
Culman, Cristian;Aminikhanghahi, Samaneh;Cook, Diane J.
对复杂活动的持续监视对于理解人类行为和提供活动感知服务是有价值的。同时，识别这些活动需要移动和位置信息，这些信息会迅速耗尽可穿戴设备的电池。本文介绍了基于变化点的活动监测（CPAM），这是一种实时识别和监测一系列简单和复杂活动的节能策略。CPAM采用无监督的变化点检测来检测可能的活动过渡时间。通过调整每个变化点的采样率，CPAM在保持连续采样的活动识别性能的同时，减少了74.64%的能量消耗。我们使用66名受试者收集和标记的智能手表数据验证了我们的方法。结果表明，变化点检测技术可以有效地减少基于传感器的移动应用程序的能量足迹，并且自动活动标签可以用于估计采样周期之间的传感器值。
Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.