Oxygen Intake Estimation in Driving Wheelchair Using Machine Learning

Abstract

Recently, in the field of sports, studies have been actively conducted to collect and analyze human behavior data from various sensors for assisting exercise. However, there are very few studies targeting disabled subjects. The purpose of this study was to suggest a model for oxygen intake estimation in driving a wheel-chair using a wearable device and to assist the exercise of wheel-chair users. The suggested model estimated the heart rate transformed from the data of 3-axis sensors using machine learning. The sensors were attached to the undercarriage of the wheel-chair. Input to the suggested model were acceleration toward a driving direction, lateral acceleration and heart rate. The suggested model estimated the heart rate every 12s. When the suggested model was applied to oxygen intake estimation during normal driving of the wheel-chair, it was confirmed that estimation was possible within 1.83 ml/ml/kg mean absolute error.