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Keeping fit has been increasingly important for people nowadays. However,people may not get expected exercise results without following professionalguidance while hiring personal trainers is expensive. In this paper, aneffective real-time system called Fitness Done Right (FDR) is proposed forhelping people exercise correctly on their own. The system includes detectinghuman body parts, recognizing exercise pose and detecting errors for test posesas well as giving correction advice. Generally, two branch multi-stage CNN isused for training data sets in order to learn human body parts andassociations. Then, considering two poses, which are plank and squat in ourmodel, we design a detection algorithm, combining Euclidean and angledistances, to determine the pose in the image. Finally, key values for keyfeatures of the two poses are computed correspondingly in the pose errordetection part, which helps give correction advice. We conduct our system inreal-time situation with error rate down to $1.2\%$, and the screenshots ofexperimental results are also presented.

Fitness Done Right: a Real-time Intelligent Personal Trainer for Exercise Correction