A Personalized Method for Calorie Consumption Assessment

TL;DR

This paper introduces a personalized, image-processing-based approach using Kinect to accurately assess calorie consumption during exercise, outperforming existing methods like Fitbit benchmarks.

Contribution

The paper presents a novel method combining Kinect-based motion capture with kinetic energy calculations for personalized calorie assessment during exercise.

Findings

Outperforms state-of-the-art calorie assessment methods

Achieves lower error rates compared to Fitbit ground-truth

Demonstrates effectiveness in broadcast gymnastics exercises

Abstract

This paper proposes an image-processing-based method for personalization of calorie consumption assessment during exercising. An experiment is carried out where several actions are required in an exercise called broadcast gymnastics, especially popular in Japan and China. We use Kinect, which captures body actions by separating the body into joints and segments that contain them, to monitor body movements to test the velocity of each body joint and capture the subject's image for calculating the mass of each body joint that differs for each subject. By a kinetic energy formula, we obtain the kinetic energy of each body joint, and calories consumed during exercise are calculated in this process. We evaluate the performance of our method by benchmarking it to Fitbit, a smart watch well-known for health monitoring during exercise. The experimental results in this paper show that our method outperforms a state-of-the-art calorie assessment method, which we base on and improve, in terms of the error rate from Fitbit's ground-truth values.