A Personalized Method for Calorie Consumption Assessment
This work addresses personalized health monitoring for individuals during exercise, but it is incremental as it builds on and improves an existing method.
The paper tackles personalized calorie consumption assessment during exercise by proposing an image-processing-based method that uses Kinect to capture body movements and calculate kinetic energy per joint. The method outperforms a state-of-the-art baseline with a lower error rate compared to Fitbit ground-truth values.
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.