Artetxe Arkaitz, Beristain Andoni, Kabongo Luis
Vicomtech-IK4 Research Centre, Mikeletegi Pasealekua 57, 20009 San Sebastian, Spain.
Stud Health Technol Inform. 2014;207:1-10.
In this work we present a system that uses the accelerometer embedded in a mobile phone to perform activity recognition, with the purpose of continuously and pervasively monitoring the users' level of physical activity in their everyday life. Several classification algorithms are analysed and their performance measured, based for 6 different activities, namely walking, running, climbing stairs, descending stairs, sitting and standing. Feature selection has also been explored in order to minimize computational load, which is one of the main concerns given the restrictions of smartphones in terms of processor capabilities and specially battery life.
在这项工作中，我们展示了一个利用手机中嵌入的加速度计来进行活动识别的系统，目的是在日常生活中持续且全面地监测用户的身体活动水平。基于六种不同的活动，即步行、跑步、爬楼梯、下楼梯、坐着和站立，分析了几种分类算法并测量了它们的性能。还探索了特征选择，以尽量减少计算负荷，鉴于智能手机在处理器能力尤其是电池寿命方面的限制，这是主要关注点之一。
