An Automatic Assessment Framework for Exercise Training System using Hidden Markov Model and K-Means Clustering

• Main Authors: Yi-RuChen, 陳奕儒
• Other Authors: Ting-Wei Hou
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/9a7nc5

碩士 === 國立成功大學 === 工程科學系 === 105 === Since the launch of RGB-D sensors, these sensors are applied to exercise training systems. They are used to record users’ exercise processes and extract human skeletal data. By monitoring/reviewing users’ exercise video or skeletal data, users or a computer program could check if the poses are correct, especially for key poses. This assessment of a key pose does not appropriately present the relationship between a user’s posture and time. This research proposes a software framework (1) for professionals to build standard reference key poses of some exercise, (2) users would perform the same exercise, and the system automatically performs assessment. This framework transforms the professionals’ demonstration into sequences of continuous movements through preprocessing, feature extracting and a clustering algorithm. These sequences of continuous movements become training data sources of Hidden Markov Models that correspond to each movement primitive. A user records his/her training process by RGB-D sensors, and through the same way above to generate sequences of the entire training process. These sequences are segmented into movement primitives, and compared to each trained HMMs. Thereby automatically assess if the training process is close to the professional’s demonstration. After viewing the feedback of training process and practicing repeatedly to reach the goal of training, the user is expected to gain improvements in the exercise.