  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.
