Volume 10, Number 5

Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs

  Authors

Itaru Kaneko¹, Yutaka Yoshida² and Emi Yuda³, ^1&2Nagoya City University, Japan and ³Tohoku University, Japan

  Abstract

The use of Holter Electrocardiograph (Holter ECG) is rapidly spreading. It is a wearable electrocardiograph that records 24-hour electrocardiograms in a built-in flash memory, making it possible to detect atrial fibrillation (Atrial Fibrillation, AF) through all-day activities. It is also useful for screening for diseases other than atrial fibrillation and for improving health. It is said that more useful information can be obtained by combining electrocardiograph with the analysis of physical activity. For that purpose, the Holter electrocardiograph is equipped with heart rate sensor and acceleration sensors. If acceleration data is analysed, we can estimate activities in daily life, such as getting up, eating, walking, using transportation, and sitting. In combination with such activity status, electrocardiographic data can be expected to be more useful.

In this study, we investigate the estimation of physical activity. For the better analysis, we evaluated activity estimation using machine learning as well as several different feature extractions. In this report, we will show several different feature extraction methods and result of human body analysis using machine learning.

  Keywords

Wearable, Biomedical Sensors, Body Activity, Machine Learning