Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Physiol Meas. 2014 Jul;35(7):1245-63. doi: 10.1088/0967-3334/35/7/1245. Epub 2014 May 22.
Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.
微型可穿戴传感器模块是一种很有前途的技术,可以在长时间内监测日常生活活动(ADL)。为了确保用户的依从性和有意义的结果,传感器的选择和放置位置需要仔细考虑。我们使用 ReSense(我们定制的惯性测量单元,增强了气压传感器,用于捕获与活动相关的高度变化),在 6 名健康受试者的实验室条件下,对 16 项 ADL 的分类进行了这些方面的研究。受试者在每个手腕和脚踝以及躯干上佩戴一个模块。活动包括全身运动以及上肢的粗大和灵巧运动。手腕模块数据在三个活动组中的表现优于其他位置。具体来说,对于所有 16 项活动,使用重复预留和用户特定验证方法,分别实现了近 93%和 95%以上的总体分类准确率。包括海拔轮廓在内,可使上下楼梯的分类准确率提高 20%左右。在这种方案下,陀螺仪对活动分类没有提供有用的信息。所提出的传感器设置可以允许在不同的患者群体中进行稳健的长期活动监测,具有较高的依从性。