Bourke Alan K, Ihlen Espen A F, Van de Ven Pepijn, Nelson John, Helbostad Jorunn L
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:4881-4884. doi: 10.1109/EMBC.2016.7591821.
We have validated a real-time activity classification algorithm based on monitoring by a body worn system which is potentially suitable for low-power applications on a relatively computationally lightweight processing unit. The algorithm output was validated using annotation data generated from video recordings of 20 elderly volunteers performing both a semi-structured protocol and a free-living protocol. The algorithm correctly identified sitting 75.1% of the time, standing 68.8% of the time, lying 50.9% of the time, and walking and other upright locomotion 82.7% of the time. This is one of the most detailed validations of a body worn sensor algorithm to date and offers an insight into the challenges of developing a real-time physical activity classification algorithm for a tri-axial accelerometer based sensor worn at the waist.
我们已经验证了一种基于可穿戴系统监测的实时活动分类算法，该系统可能适用于相对计算轻量级处理单元上的低功耗应用。算法输出使用20名老年志愿者进行半结构化协议和自由生活协议的视频记录生成的注释数据进行验证。该算法正确识别坐姿的时间占75.1%，站姿的时间占68.8%，躺姿的时间占50.9%，行走和其他直立运动的时间占82.7%。这是迄今为止对可穿戴传感器算法最详细的验证之一，并深入了解了为腰部佩戴的基于三轴加速度计的传感器开发实时身体活动分类算法所面临的挑战。
