Filippoupolitis Avgoustinos, Oliff William, Takand Babak, Loukas George
Computing and Information Systems Department, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK.
Sensors (Basel). 2017 May 27;17(6):1230. doi: 10.3390/s17061230.
Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.
室内空间中的活动识别有助于情境感知，并提高与个性化健康监测、建筑能源管理、安全保障相关应用的效率。然而，大多数活动识别框架采用的是专门的建筑传感器网络或可穿戴式传感器网络。由于这种方法在实用性方面存在问题，我们建议使用现成的商用设备。在这项工作中，我们设计并评估了一个由智能手表组成的活动识别系统，该智能手表通过来自低功耗蓝牙（BLE）信标的位置信息得到增强。我们使用四种监督式机器学习算法，在室内实验室环境中对各种活动执行情况评估了这种方法的性能。我们的实验结果表明，我们的位置增强型活动识别系统能够达到92%至100%的分类准确率，而在没有位置信息的情况下，根据为数据分割选择的窗口大小，分类准确率在某些情况下可能会降至低至50%。
