Volume 10, Number 3

Machine-Learning Estimation of Body Posture and Physical Activity by Wearable Acceleration and Heartbeat Sensors

  Authors

Yutaka Yoshida², Emi Yuda^{3, 1}, Kento Yamamoto⁴, Yutaka Miura⁵ and Junichiro Hayano¹, ¹Nagoya City University Graduate School of Medical Science, Japan, ²Nagoya City University Graduate School of Design and Architecture, Japan, ³Tohoku University Graduate School of Engineering, Japan, ⁴University of Tsukuba Graduate School of Comprehensive Human Sciences, Japan and ⁵Shigakkan University, Japan

  Abstract

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.

  Keywords

Accelerometer, Holter ECG, Posture, Activity, Machine learning, Random Forest, R-R interval