The article proposes a new framework for assessment of physical rehabilitation exercises based on a deep learning approach. The objective of the framework is automated quantification of patient performance in completing prescribed rehabilitation exercises, based on captured whole-body joint trajectories. The main components of the framework are metrics for measuring movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for regressing quality scores of input movements via supervised learning. Furthermore, an overview of the existing methods for modeling and evaluation of rehabilitation movements is presented, encompassing various distance functions, dimensionality-reduction techniques, and movement models employed for this problem in prior studies. To the best of our knowledge, this is the first work that implements deep neural network for assessment of rehabilitation performance. Multiple deep network architectures are repurposed for the task in hand and are validated on a dataset of rehabilitation exercises.
Several studies in the literature on exercise evaluation employed machine learning methods to classify the individual repetitions into of movements. Methods used for this purpose include Adaboost classifier @cite_18 , @math -nearest neighbors @cite_30 , Bayesian classifier @cite_26 , and an ensemble of multi-layer perceptron NNs @cite_32 . The outputs in these approaches are discrete class values of @math or @math (i.e., incorrect or correct repetition). However, these methods do not provide the capacity to detect varying levels of movement quality or identify incremental changes in patient performance over the program duration.
@cite_30 @cite_18 @cite_26 @cite_32
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In this paper, we describe methods for assessment of exercise quality using body-worn tri-axial accelerometers. We assess exercise quality by building a classifier that labels incorrect exercises. The incorrect performances are divided into a number of classes of errors as defined by a physical therapist. We focus on exercises commonly prescribed for knee osteoarthritis: standing hamstring curl, reverse hip abduction, and lying straight leg raise. The methods presented here will form the basis for an at-home rehabilitation device that will recognize errors in patient exercise performance, provide appropriate feedback on the performance, and motivate the patient to continue the prescribed regimen. Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained. Recent advances of robotic mechanical devices enable us to measure a subjectpsilas performance in an objective and precise manner. The main issue of using such devices is how to represent huge experimental data compactly in order to analyze and compare them with clinical data efficiently. In this paper, we choose a subset of features from real-time experimental data and build a classifier model to assess stroke patientspsila upper limb functionality. We compare our model with combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our results from experimental data are consistent with clinical information, and can capture changes of upper-limb functionality over time.