Guiry John J, van de Ven Pepijn, Nelson John
Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland.
Sensors (Basel). 2014 Mar 21;14(3):5687-701. doi: 10.3390/s140305687.
In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices' ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances.
在本文中，作者研究了包括智能手机和智能手表在内的智能设备在识别日常生活活动中所能发挥的作用。开展了一项涉及N = 10名参与者的可行性研究，以评估这些设备区分九种日常活动的能力。所检测的活动包括步行、跑步、骑自行车、站立、坐着、乘坐电梯上升、乘坐电梯下降、爬楼梯和下楼梯。作者还评估了这些设备区分室内和室外环境的能力，旨在增强情境感知。本研究的数据用于训练和测试五种著名的机器学习算法：C4.5、CART、朴素贝叶斯、多层感知器，最后是支持向量机。研究了单传感器和多传感器方法，以更好地理解设备中的每个传感器在非侵入式活动识别中所能发挥的作用。作者发现总体结果很有前景，一些模型对所有实例的正确分类率高达100%。
