Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest 060042 , Romania.
National Institute for Research and Development in Informatics, Bucharest 011455 , Romania.
Sensors (Basel). 2019 Jan 23;19(3):458. doi: 10.3390/s19030458.
Because the number of elderly people is predicted to increase quickly in the upcoming years, "aging in place" (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.
由于预计未来几年老年人数量将迅速增加，“原地老龄化”（指无论年龄和其他因素如何都在家中生活）在环境辅助生活领域正成为一个重要话题。因此，在本文中，我们提出了一种基于智能手机传感器收集数据的人体活动识别系统。该方法意味着使用智能手机上的三个传感器（加速度计、陀螺仪和重力传感器）开发一个分类器。我们选择在移动电话上实现我们的解决方案，因为它们无处不在，并且不需要受试者携带可能妨碍其活动的额外传感器。对于我们的建议，我们的目标是行走、跑步、坐着、站立、上下楼梯。我们针对两个数据集（我们内部收集的一个和外部收集的一个）进行了评估，效果很好。结果表明，识别所有六种活动的准确率都很高，特别是在行走、跑步、坐着和站立方面的结果非常好。该系统已完全在移动设备上实现，作为一个 Android 应用程序。
