Validation of a smart mirror for gesture recognition in gym training performed by a vision-based deep learning system

This paper illustrates the development and validation of a smart mirror for sports training. The application is based on the skeletonization algorithm MediaPipe and runs on an embedded device Nvidia Jetson Nano equipped with two fisheye cameras. The software has been evaluated considering the exercise biceps curl. The elbow angle has been measured by both MediaPipe and the motion capture system BTS (ground truth), and the resulting values have been compared to determine angle uncertainty, residual errors, and intra-subject and inter-subject repeatability. The uncertainty of the joints’ estimation and the quality of the image captured by the cameras reflect on the final uncertainty of the indicator over time, highlighting the areas of improvement for further development.