Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body.
Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body.
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DOI：
10.1371/journal.pone.0130851
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Kattan A
中科院分区：
文献类型：
--
作者：
Arif M;Kattan A
使用无线传感器监测身体活动有助于识别真实环境中的姿势方向和运动。本文提出了一种简单而稳健的基于时域特征的身体活动识别方法，该方法将传感器放置在受试者的手腕、胸部和脚踝上。提出了一种基于加速度传感器记录的加速度信号的时域特性的特征集，用于对12种身体活动进行分类。9名受试者进行了12种不同类型的体育活动，包括坐、站、走、跑、骑自行车、北欧散步、爬楼梯、下楼梯、吸尘器、熨衣服和跳绳，以及躺下(休息状态)。年龄27.2±3.3岁，体重指数(BMI)25.11±2.6 kg/m2。分类结果显示，所有体力活动的精确度(阳性预测值)和召回率(敏感度)都超过95%，具有很高的有效性。对于三个传感器的组合特征集，总体分类准确率为98%。建议的框架可用于监测受试者的体力活动，这对卫生专业人员评估健康人和患者的体力活动非常有用。
Monitoring physical activities by using wireless sensors is helpful for identifying postural orientation and movements in the real-life environment. A simple and robust method based on time domain features to identify the physical activities is proposed in this paper; it uses sensors placed on the subjects’ wrist, chest and ankle. A feature set based on time domain characteristics of the acceleration signal recorded by acceleration sensors is proposed for the classification of twelve physical activities. Nine subjects performed twelve different types of physical activities, including sitting, standing, walking, running, cycling, Nordic walking, ascending stairs, descending stairs, vacuum cleaning, ironing clothes and jumping rope, and lying down (resting state). Their ages were 27.2 ± 3.3 years and their body mass index (BMI) is 25.11 ± 2.6 Kg/m2. Classification results demonstrated a high validity showing precision (a positive predictive value) and recall (sensitivity) of more than 95% for all physical activities. The overall classification accuracy for a combined feature set of three sensors is 98%. The proposed framework can be used to monitor the physical activities of a subject that can be very useful for the health professional to assess the physical activity of healthy individuals as well as patients.