A Distributed Gamified System Based on Automatic Assessment of Physical Exercises to Promote Remote Physical Rehabilitation

Abstract

Physical rehabilitation aims at improving the functional ability and quality of life of patients affected by physical impairments or disabilities. Neurological diseases represent the largest cause of disability worldwide. For many, there is no cure and physiotherapy allows symptoms to be managed. Physiotherapy is based on the daily execution of exercises, traditionally under the supervision of a therapist. However, performing these exercises requires that both the patient and the physiotherapist are together so that the physiotherapist can assist the patient while exercising. For patients with a neurological condition, rehabilitation is a long term process, lasting months or even years. Not withstanding the personal costs, the cost of care/physiotherapy is high and represents e27,711 per year in Spain. This is compounded by a shortage of qualified therapists, often cited as one reason why stroke survivors do not received the recommended amount of therapy. The challenge is even greater in low to mid-income countries where there is a lack of trained personnel as well as under-served and remote regions. Technology can be employed to alleviate these problems by remotely monitoring a rehabilitation session taking place at home or anywhere in the community. This paper presents a computer vision-based system for home-use that automatically assesses how well the patient performs the exercises and transmits the information back to the clinic. The patient and physiotherapist do not need to be co-located. Gamification methods and techniques are used to engage patients when carrying out the rehabilitation routines. To this end, we propose a distributed gamified system that automatically evaluates the performance of exercises by analyzing and comparing motion curves using the DTW (Dynamic Time Warping) algorithm. INDEX TERMS Assistive technologies, remote physical rehabilitation, gamification, dynamic time warping (DTW).

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

“…One technique for dealing with this is dynamic time warping [104], which can handle temporal sequences of different lengths. This has been a widely used technique of past assessment tasks [100], [103], [105], [106] and even in recent works [107], [108]. A better solution was proposed by Zhu et al [97] that trained a separate CNN network, D-CNN, to deal with dynamic input lengths.…”

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“…Naive Bayes [22] Dynamic Time Warping [36] Exergame Classification [29] K-nearest neighbors [3] Markov Model & Dynamic Type Warping [37] Decision tree [38] Support Vector Machine [39] Classification [7] K-Means [15] Mobile Apps Markov Chain Analysis [23] Game classification [40] Support Vector Machine [2] K-Nearest Neighbours [2] Classification [5] Logical Regression [33] available least accurate Most Accurate…”

“…Gamified application Dynamic Time Warping [36] 29. An This study applies machine learning algorithms and surface electromyography (sEMG) to the rehabilitation system.…”

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“…The skeleton data are also used for motion monitoring and injury prevention [101]. Rehabilitation monitoring systems assist patients during recovery [102] and increase their engagement via gamification [103].…”