Ermes Miikka, Parkka Juha, Cluitmans Luc
VTT Technical Research Centre of Finland, P.O. Box 1300, FI-33101, Tampere, Finland.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:4451-4. doi: 10.1109/IEMBS.2008.4650199.
Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application.
利用可穿戴传感器进行活动识别可以激励人们进行各种不同的运动和其他体育锻炼。我们之前已经开发了用于对可穿戴传感器收集的活动数据进行离线分析的算法。在本文中，我们展示了将现有算法的平台推进到在线版本并移植到个人数字助理（PDA）上的当前进展。加速度数据从无线运动手环获取，这些手环通过蓝牙链路将3D原始加速度信号发送到PDA，然后由PDA进行数据收集、特征提取和活动分类。作为概念验证，在线活动系统对三名受试者进行了测试。他们所有人都对以下每项活动至少进行了5分钟：躺卧、坐着、站立、行走、跑步以及使用健身自行车骑行。受试者的平均逐秒分类准确率分别为99%、97%和82%。这些结果表明，之前开发的用于对从可穿戴传感器获得的加速度数据进行离线分析的方法可以成功应用于在线活动识别应用中。
