Mannini Andrea, Sabatini Angelo Maria
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3302-5. doi: 10.1109/IEMBS.2011.6090896.
The awareness of the physical activity that human subjects perform, and the quantification of activity strength and duration are important tasks that a wearable sensor system would fulfill to be valuable in several biomedical applications, from health monitoring to physical medicine and rehabilitation. In this work we develop a wearable sensor system that collect data from a single thigh-mounted tri-axial accelerometer; the system performs activity classification (sit, stand, cycle, walk, run), and speed estimation for walk (run) labeled data features. These classification/estimation tasks are achieved by cascading two Support Vector Machines (SVM) classifiers. Activity classification accuracy higher than 99% and root mean square errors E(RMS) = 0.28 km/h for speed estimation are obtained in our preliminary experiments. The developed wearable sensor system provides activity labels and speed point estimates at the pace of two readings per second.
了解人类受试者进行的身体活动，并对活动强度和持续时间进行量化，是可穿戴传感器系统在从健康监测到物理医学与康复等多种生物医学应用中发挥价值时需要完成的重要任务。在这项工作中，我们开发了一种可穿戴传感器系统，该系统从安装在单一大腿上的三轴加速度计收集数据；该系统对活动进行分类（坐、站、骑自行车、步行、跑步），并对标记为步行（跑步）的数据特征进行速度估计。这些分类/估计任务通过级联两个支持向量机（SVM）分类器来实现。在我们的初步实验中，活动分类准确率高于99%，速度估计的均方根误差E(RMS)=0.28公里/小时。所开发的可穿戴传感器系统以每秒两次读数的速度提供活动标签和速度点估计。
