文章基本信息

标题：Machine-Learning Estimation of Body Posture and Physical Activity by Wearable Acceleration and Heartbeat Sensors
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作者：Yutaka Yoshida ; Emi Yuda ; Kento Yamamoto 等
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2019
卷号：10
期号：3
页码：1-9
DOI：10.5121/sipij.2019.10301
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：We aimed to develop the method for estimating body posture and physical activity by acceleration signals from a Holter electrocardiographic (ECG) recorder with built-in accelerometer. In healthy young subjects, triaxial-acceleration and ECG signal were recorded with the Holter ECG recorder attached on their chest wall. During the recording, they randomly took eight postures, including supine, prone, left and right recumbent, standing, sitting in a reclining chair, sitting in chairs with and without backrest, and performed slow walking and fast walking. Machine learning (Random Forest) was performed on acceleration and ECG variables. The best discrimination model was obtained when the maximum values and standard deviations of accelerations in three axes and mean R-R interval were used as feature values. The overall discrimination accuracy was 79.2% (62.6-90.9%). Supine, prone, left recumbent, and slow and fast walk were discriminated with >80% accuracy, although sitting and standing positions were not discriminated by this method..
关键词：Accelerometer; Holter ECG; Posture; Activity; Machine learning; Random Forest; R;R interval