Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers
Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers
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DOI：
10.1109/jsen.2017.2722105
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发表时间：
2017-08-15
影响因子：
4.3
通讯作者：
Munoz-Organero, Mario
中科院分区：
文献类型：
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作者：
Fullerton, Elliott;Heller, Ben;Munoz-Organero, Mario
识别人类活动对于研究人员了解患者的行为非常有用，并且可以帮助在未来的建议中规定活动。已证明使用身体佩戴式加速度计可以准确测量人类活动;然而，在自由生活环境中使用多个身体佩戴式加速度计来识别各种活动的研究并不明显。本文旨在通过在自由生活环境中使用多个身体佩戴的加速度计来成功地识别活动和子类别活动类型。10名参与者（年龄= 23.1 +/- 1.7岁，身高= 171.0 +/- 4.7 cm，体重= 78.2 +/- 12.5 Kg）佩戴9个身体佩戴式加速度计，自由生活一天。活动类型通过使用可穿戴摄像头进行识别，通过自由生活和受控测试相结合的方式对子类活动进行量化。测试了各种机器学习技术，包括预处理算法、特征和分类器选择，并报告了准确性和计算时间。未过滤数据的平均值和标准差特征的精细k-近邻分类器报告的识别准确率为97.6%。受控和自由生活测试为子类活动提供了高度准确的识别（> 95.0%）。决策树分类器和最大特征被证明具有最低的计算时间。结果表明，通过使用多个身体佩戴的加速度计，可以在自由生活的环境中识别活动和子类别活动类型。这种方法可以帮助处方建议活动和久坐不动的时期健康的生活。
Recognizing human activity is very useful for an investigator about a patient's behavior and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity; however, research looking at the use of multiple body worn accelerometers in a free living environment to recognize a wide range of activities is not evident. This paper aimed to successfully recognize activity and sub-category activity types through the use of multiple body worn accelerometers in a free-living environment. Ten participants (Age = 23.1 +/- 1.7 years, height = 171.0 +/- 4.7 cm, and mass = 78.2 +/- 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and subcategory activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of preprocessing algorithms, feature, and classifier selections were tested, accuracy, and computing time were reported. A fine k-nearest neighbor classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities (> 95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Results show that recognition of activity and sub-category activity types is possible in a free-living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living.