Fitness Done Right: a Real-time Intelligent Personal Trainer for Exercise Correction
It addresses the need for affordable exercise guidance for individuals, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of providing real-time exercise correction without a personal trainer by proposing Fitness Done Right (FDR), a system that detects poses like plank and squat and gives correction advice, achieving an error rate of 1.2% in real-time tests.
Keeping fit has been increasingly important for people nowadays. However, people may not get expected exercise results without following professional guidance while hiring personal trainers is expensive. In this paper, an effective real-time system called Fitness Done Right (FDR) is proposed for helping people exercise correctly on their own. The system includes detecting human body parts, recognizing exercise pose and detecting errors for test poses as well as giving correction advice. Generally, two branch multi-stage CNN is used for training data sets in order to learn human body parts and associations. Then, considering two poses, which are plank and squat in our model, we design a detection algorithm, combining Euclidean and angle distances, to determine the pose in the image. Finally, key values for key features of the two poses are computed correspondingly in the pose error detection part, which helps give correction advice. We conduct our system in real-time situation with error rate down to $1.2\%$, and the screenshots of experimental results are also presented.