Patients’ follow-up using biomechanical analysis of rehabilitation exercises

Bonnechère, Bruno; Jansen, Bart; Omelina, Luboš; Sholukha, Victor; Jan, Serge Van Sint

doi:10.17083/ijsg.v4i1.121

Cited by 9 publications

(19 citation statements)

References 26 publications

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“…The mean QualSyst score is 86.42 -7.65%. Furthermore, 2017 reported the highest number of published articles (seven articles [91][92][93][94][95][96][97] ).…”

Section: Vision Systemsmentioning

confidence: 99%

Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review

Aguilar-Lazcano

Rechy-Ramirez

et al. 2019

Games for Health Journal

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This systematic review aims to analyze the state-of-the-art regarding interaction modalities used on serious games for upper limb rehabilitation. A systematic search was performed in IEEE Xplore and Web of Science databases. PRISMA and QualSyst protocols were used to filter and assess the articles. Articles must meet the following inclusion criteria: they must be written in English; be at least four pages in length; use or develop serious games; focus on upper limb rehabilitation; and be published between 2007 and 2017. Of 121 articles initially retrieved, 33 articles met the inclusion criteria. Three interaction modalities were found: vision systems (42.4%), complementary vision systems (30.3%), and no-vision systems (27.2%). Vision systems and no-vision systems obtained a similar mean QualSyst (86%) followed by complementary vision systems (85.7%). Almost half of the studies used vision systems as the interaction modality (42.4%) and used the Kinect sensor to collect the body movements (48.48%). The shoulder was the most treated body part in the studies (19%). A key limitation of vision systems and complementary vision systems is that their device performances might be affected by lighting conditions. A main limitation of the no-vision systems is that the range-of-motion in angles of the body movement might not be measured accurately. Due to a limited number of studies, fruitful areas for further research could be the following: serious games focused on finger rehabilitation and trauma injuries, game difficulty adaptation based on user's muscle strength and posture, and multisensor data fusion on interaction modalities.

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“…The mean QualSyst score is 86.42 -7.65%. Furthermore, 2017 reported the highest number of published articles (seven articles [91][92][93][94][95][96][97] ).…”

Section: Vision Systemsmentioning

confidence: 99%

Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review

Aguilar-Lazcano

Rechy-Ramirez

et al. 2019

Games for Health Journal

View full text Add to dashboard Cite

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“…In addition, [25] found, through selecting and analyzing developed gamified systems, that there exists a tendency to develop low-cost solutions for in-home exercising with various games [8] [26]. In [27], the methodology was to use data collected during SG exercises for later analysis in order to monitor the long-term evolution of patients.…”

Section: State-of-the-artmentioning

confidence: 99%

Development of a Hardware/Software System for Proprioception Exergaming

Kobeissi

Lanza²,

Berta³

et al. 2018

IJSG

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Physiotherapy and rehabilitation are getting ever more relevance as an integral part of healthcare devising and employing new, effective methodologies and tools. However, not seldom are such tools boring and difficult to use properly for achieving the given training and therapeutic goals. Exergaming, the combination of physical exercise and video gaming, may help to overcome these issues by offering enjoyment and motivation. However, the literature lacks papers investigating how to integrate such devices into a serious game combining effectiveness and enjoyment. In this paper, we focus on the Balance Board (BB), a device used in gyms and in several health-related applications [1], and present an exergaming system for proprioception training. The system includes a BB, which was instrumented to act as a motion controller for an ad-hoc developed, simple 3D video game. The system aims to provide enjoyable training with the BB, boosting equilibrium exercising through the simulation of downhill skiing. We validated the score computation and tested the efficacy of the system in a user test with 40 participants, in ecological settings. The evaluation concerned user performance and a game experience questionnaire. The results showed statistically significant improvement in players’ ability to use the BB. Users also reported a positive gameplay experience.

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“…Previous studies have shown that the Kinect sensor could be used to follow patients’ evolution during rehabilitation exercises [ 26 , 27 ]. This kind of evaluation, done during the rehabilitation, has many advantages: (i) it is done in the natural environment of the patient (it is known that patients are not exhibiting the same performance when they are wearing underwear in a gait laboratory); (ii) when patients are immersed in the games they are less focused on the motion and on pain and can reach larger amplitudes than when they are asked to perform one particular motion; (iii) it provides time savings; and (iv) it is financially beneficial (the devices are affordable, and since the evaluation is done within the therapy session there is no dual pricing) [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application

Bonnechère

Sholukha

Omelina

et al. 2018

Sensors

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Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The KinectTM sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the KinectTM were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper.

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Patients’ follow-up using biomechanical analysis of rehabilitation exercises

Cited by 9 publications

References 26 publications

Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review

Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review

Development of a Hardware/Software System for Proprioception Exergaming

3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application

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