Department for Information Technology, American University in Bosnia and Herzegovina, 75000 Tuzla, Bosnia and Herzegovina.
Department of Telecommunications, Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.
Sensors (Basel). 2019 Nov 27;19(23):5206. doi: 10.3390/s19235206.
Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.
人体活动识别（HAR）是一种用于识别人体运动的分类过程。本文全面回顾了 HAR 每个阶段目前考虑的方法，以及每个 HAR 阶段对能耗和延迟的影响。它强调了文献中用于优化 HAR 各个阶段能耗和延迟的各种方法，并进行了分析，为在健康和福祉应用中实施 HAR 提供了方向。本文分析了 HAR 过程的每个阶段是否以及如何影响能耗和延迟。结果表明，数据收集和过滤以及数据分段和分类是在能耗和延迟之间取得平衡的关键阶段。由于延迟仅对实时 HAR 应用程序很关键，因此传感器和设备的能耗是 HAR 在健康和福祉应用中实施的关键挑战。克服与 HAR 实施相关的挑战的大多数方法都发生在数据收集、过滤和分类阶段，而数据分段阶段需要进一步探索。最后，本文建议在考虑目标人群的背景和健康状况的情况下，在健康和福祉应用中实现 HAR 时在能耗和延迟之间取得平衡。
