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.
The majority of related studies employed for deriving movement quality scores. Concretely, @cite_22 used a variant of the Mahalanobis distance to quantify the level of correctness of rehabilitation movements, based on a calculated distance between patient-performed repetitions and a set of repetitions performed by a group of healthy individuals. Similarly, a body of work utilized the dynamic time warping (DTW) algorithm @cite_35 for calculating the distance between a patients performance and healthy subjects performance @cite_34 -- @cite_17 . The advantage of the distance functions is that they are not exercise-specific, and thus can be applied for assessment of new types of exercises. However, the distance functions also have shortcomings, because they do not attempt to derive a model of the rehabilitation data, and the distances are calculated at the level of individual time-steps in the raw measurements.
@cite_35 @cite_34 @cite_22 @cite_17
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This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm. Most formal rehabilitation facilities are situated in a hospital or care center setting, which may not always be conveniently accessible for patients, especially those in geographically isolated areas. Home-based rehabilitation has potential to offer greater accessibility and thus increase consistent uptake. In addition, the exercise performed in conventional rehabilitation contexts may be insufficient to ensure the patient's speedy recovery, with complimentary rehabilitation exercises at home required to make a difference. The goal is to provide effective home-based rehabilitation offering outcomes similar to those obtained through hospital-based rehabilitation under the supervision of an occupational therapist. This paper presents the development of a Kinect-based system for ensuring home-based rehabilitation using a Dynamic Time Warping (DTW) algorithm and fuzzy logic. The ultimate goal is to assist patients in conducting safe and effective home-based rehabilitation without the immediate supervision of a physician. Mobility improvement for patients is one of the primary concerns of physiotherapy rehabilitation. Providing the physiotherapist and the patient with a quantified and objective measure of progress can be beneficial for monitoring the patient's performance. In this paper, two approaches are introduced for quantifying patient performance. Both approaches formulate a distance between patient data and the healthy population as the measure of performance. Distance measures are defined to capture the performance of one repetition of an exercise or multiple repetitions of the same exercise. To capture patient progress across multiple exercises, a quality measure and overall score are defined based on the distance measures and are used to quantify the overall performance for each session. The effectiveness of these measures in detecting patient progress is evaluated on rehabilitation data recorded from patients recovering from knee or hip replacement surgery. The results show that the proposed measures are able to capture the trend of patient improvement over the course of rehabilitation. The trend of improvement is not monotonic and differs between patients.