Adaptive Motion Gaming AI for Health Promotion

This paper presents a design of a non-player character (AI) for promoting balancedness in use of body segments when engaging in full-body motion gaming. In our experiment, we settle a battle between the proposed AI and a player by using FightingICE, a fighting game platform for AI development. A middleware called UKI is used to allow the player to control the game by using body motion instead of the keyboard and mouse. During gameplay, the proposed AI analyze health states of the player; it determines its next action by predicting how each candidate action, recommended by a Monte-Carlo tree search algorithm, will induce the player to move, and how the player's health tends to be affected. Our result demonstrates successful improvement in balancedness in use of body segments on 4 out of 5 subjects.Comment: A revised version of our paper for 2017 AAAI Spring Symposium Series (Well-Being AI: From Machine Learning to Subjective Oriented Computing), San Francisco,USA, Mar. 27-29, 2017. Revised contents, due to our correction of (8), are highlighted in red. Many apologies, but the effectiveness of the proposed method/approach in the paper still hold

A Personalized Method for Calorie Consumption Assessment

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.Comment: The AAAI 2018 Spring Symposium on Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being, March 26-28, 2018, Stanford University, Palo Alto, California US