A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions

In physical rehabilitation, the progress in machine learning and the advent of affordable and low-cost motion capture solutions have been conducive to the development of approaches for automatedmonitoring of patient performance.A system for rehabilitation exercise repetition counting and validation based on a set of skeleton-based features that are obtained from a 2D human pose estimation network was proposed. A deeplearning application was developed to provide real-time feedback with much more specific informationregarding exactly how the motion deviates from the correct execution. To this end, a dataset of 6resistance training exercises was acquired to train a deep neural network to predict the exercises'movements at a frame level. Hence, this underlying idea of inferring the moment of the exercise istwo-fold: (i) to provide information about the exercise execution with a fine level of detail; (ii) theability to detect invalid repetitions promptly. Finally, a repetition counting and a validation modulereceive the predicted moment and output the current number of valid repetitions.The proposed system showed good performance in motion analysis resulting in an unobtrusivemonitoring and evaluation method that could be tested for rehabilitation scopes in-home basedunsupervised settings.The development of systems that can reliably capture human movements, automatically analyzethe recorded data and evaluate the quality of the movement performance will play an important rolein supplementing traditional rehabilitation assessments performed by trained clinicians and assistingpatients participating in home-based rehabilitation.