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A new adaptive VR-based exergame for hand rehabilitation after stroke

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Abstract

The aim of this work is to present an adaptive serious game based on virtual reality (VR) for functional rehabilitation of the hand after stroke. The game focuses on simulating the palmar gras** exercise commonly used in clinical settings. The system’s design follows a user-centered approach, involving close collaboration with functional rehabilitation specialists and stroke patients. It uses the Leap motion controller to enable patient interaction in the virtual environment, which was created using the Unity 3D game engine. The system relies on hand gestures involving opening and closing movements to interact with virtual objects. It incorporates parameters to objectively measure participants’ performance throughout the game session. These metrics are used to personalize the game’s difficulty to each patient’s motor skills. To do this, we implemented an approach that dynamically adjusts the difficulty of the exergame according to the patient’s performance during the game session. To achieve this, we used an unsupervised machine learning technique known as clustering, in particular using the K-means algorithm. By applying this technique, we were able to classify patients’ performance into distinct groups, enabling us to assess their skill level and adapt the difficulty of the game accordingly. To evaluate the system’s effectiveness and reliability, we conducted a subjective evaluation involving 11 stroke patients. The standardized System Usability Scale (SUS) questionnaire was used to assess the system’s ease of use, while the Intrinsic Motivation Inventory (IMI) was used to evaluate the participants’ subjective experience with the system. Evaluations showed that our proposed system is usable and acceptable on a C-level scale, with a good adjective score, and the patients perceived a high intrinsic motivation.

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Fig. 1

(Reprinted from NeuroRehabilitation, vol. 48, no. 3, Ana de Los Reyes-Guzmán, Vicente Lozano-Berrio, María Alvarez-Rodríguez, Elisa López-Dolado, Silvia Ceruelo-Abajo, Francisco Talavera-Díaz, Angel Gil-Agudo, RehabHand: Oriented-tasks serious games for upper limb rehabilitation by using Leap Motion Controller and target population in spinal cord injury, pp. 365–373, Copyright (2021), with permission from IOS Press. The publication is available at IOS Press through https://doi.org/10.3233/NRE-201598)

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Data availability

The data supporting the results of this study are available on request. Researchers interested in these data can contact me at “amalbouatrous@gmail.com”.

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Acknowledgements

The authors would like to thank Pr Houria Kaced, Dr Amine Brahimi, Dr Sara Ait Ziane (Functional rehabilitation service, CHU Douera, Algiers, Algeria), and Dr Olivier Rémy-Néris, Dr Myriam THIEBAUT-CLERIN (Neurological MPR Service CHRU Brest, France), for their helpful advices, meticulous specifications, and exceptional collaboration demonstrated throughout the entirety of the system design process. The authors are very grateful to the patients who contributed to the evaluation and improvement of their proposed system.

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Author notes

  1. Abdelkrim Meziane, Nadia Zenati and Chafiaa Hamitouche have contributed equally to this work.

Authors and Affiliations

  1. Computer Science Faculty, University of Science and Technology Houari Boumediene (USTHB), BP 32 El Alia, Bab Ezzouar, 16111, Algiers, Algeria

    Amal Bouatrous

  2. Research Center on Scientific and Technical Information (CERIST), Algiers, Algeria

    Amal Bouatrous & Abdelkrim Meziane

  3. Centre de Développement des Technologies Avancées (CDTA), Algiers, Algeria

    Nadia Zenati

  4. IMT-Atlantique Bretagne- Pays de la Loire, Brest, France

    Amal Bouatrous & Chafiaa Hamitouche

  5. LaTIM, INSERM, UMR, 1101, Brest, France

    Amal Bouatrous & Chafiaa Hamitouche

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  1. Amal Bouatrous
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  2. Abdelkrim Meziane
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Correspondence to Amal Bouatrous.

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Communicated by Y. Zhang.

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Bouatrous, A., Meziane, A., Zenati, N. et al. A new adaptive VR-based exergame for hand rehabilitation after stroke. Multimedia Systems 29, 3385–3402 (2023). https://doi.org/10.1007/s00530-023-01180-0

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