Learning human motion feedback with neural self-organization

The correct execution of well-defined movements in sport disciplines may increase the body´s mechanical efficiency and reduce the risk of injury. While there exists an extensive number of learning-based approaches for the recognition of human actions, the task of computing and providing feedback for correcting inaccurate movements has received significantly less attention in the literature. We present a learning system for automatically providing feedback on a set of learned movements captured with a depth sensor. The proposed system provides visual assistance to the person performing an exercise by displaying real-time feedback to correct possible inaccurate postures and motion. The learning architecture uses recursive neural network self-organization extended for predicting the correct continuation of the training movements. We introduce three mechanisms for computing feedback on the correctness of overall movement and individual body joints. For evaluation purposes, we collected a data set with 17 athletes performing 3 powerlifting exercises. Our results show promising system performance for the detection of mistakes in movements on this data set.