Methods Inf Med - Exercise recognition for Kinect-based telerehabilitation.

Tópicos

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Resumo

CKGROUND: An aging population and people's higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users.OBJECTIVES: Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a friendly interface oriented both to users and physiotherapists.METHODS: The two main constituents of our algorithm are the posture classification method and the exercises recognition method. The exercises consist of series of movements. Each movement is composed of an initial posture, a final posture and the angular trajectories of the limbs involved in the movement. The algorithm was designed and tested with datasets of real movements performed by volunteers. We also explain in the paper how we obtained the optimal values for the trade-off values for posture and trajectory recognition.RESULTS: Two relevant aspects of the algorithm were evaluated in our tests, classification accuracy and real-time data processing. We achieved 91.9% accuracy in posture classification and 93.75% accuracy in trajectory recognition. We also checked whether the algorithm was able to process the data in real-time. We found that our algorithm could process more than 20,000 postures per second and all the required trajectory data-series in real-time, which in practice guarantees no perceptible delays. Later on, we carried out two clinical trials with real patients that suffered shoulder disorders. We obtained an exercise monitoring accuracy of 95.16%.CONCLUSIONS: We present an exercise recognition algorithm that handles the data provided by Kinect efficiently. The algorithm has been validated in a real scenario where we have verified its suitability. Moreover, we have received a positive feedback from both users and the physiotherapists who took part in the tests.

Resumo Limpo

ckground age popul peopl higher surviv diseas trauma leav physic consequ challeng aspect context effici health manag telerehabilit system develop allow monitor support physiotherapi session home reduc healthcar cost also improv qualiti life usersobject goal develop kinectbas algorithm provid accur realtim monitor physic rehabilit exercis also provid friend interfac orient user physiotherapistsmethod two main constitu algorithm postur classif method exercis recognit method exercis consist seri movement movement compos initi postur final postur angular trajectori limb involv movement algorithm design test dataset real movement perform volunt also explain paper obtain optim valu tradeoff valu postur trajectori recognitionresult two relev aspect algorithm evalu test classif accuraci realtim data process achiev accuraci postur classif accuraci trajectori recognit also check whether algorithm abl process data realtim found algorithm process postur per second requir trajectori dataseri realtim practic guarante percept delay later carri two clinic trial real patient suffer shoulder disord obtain exercis monitor accuraci conclus present exercis recognit algorithm handl data provid kinect effici algorithm valid real scenario verifi suitabl moreov receiv posit feedback user physiotherapist took part test

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