School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752, USA.
Sensors (Basel). 2020 Jan 6;20(1):310. doi: 10.3390/s20010310.
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.
连续监测复杂活动对于理解人类行为和提供活动感知服务非常有价值。同时，识别这些活动需要运动和位置信息，这会快速耗尽可穿戴设备的电池。在本文中，我们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 使用无监督的变化点检测来检测可能的活动转换时间。通过在每个变化点自适应采样率，CPAM 在保留连续采样的活动识别性能的同时，将能耗降低了 74.64%。我们使用由 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，变化点检测技术可有效降低基于传感器的移动应用程序的能源足迹，并且可以使用自动活动标签来估计采样期间之间的传感器值。
