cirpublications.com-B7XV-1790874883-j881480578 1790874883 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2026 5 2 Lucas Fernandez Diego Martinez Pablo Ruiz 2026 10.68159/j881480578 https://cirpublications.com/pub/journal/1/article/j881480578 Huber SK, Held JP, de Bruin ED, Knols RH. Personalized motor-cognitive exergame training in chronic stroke patients—a feasibility study. Front Aging Neurosci. 2021;13:730801. 10.3389/fnagi.2021.730801 Nguyen AV, Ong YL, Luo CX, Thuraisingam T, Rubino M, Levin MF, et al. Virtual reality exergaming as adjunctive therapy in a sub-acute stroke rehabilitation setting: facilitators and barriers. Disabil Rehabil Assist Technol. 2019;14(8):1-12. 10.1080/17483107.2019.1618403 Lu HY, Wang X, Hu C, Lau CC, Tong RK. Home-based guidance training system with interactive visual feedback using Kinect on stroke survivors with moderate to severe motor impairment. J Neuroeng Rehabil. 2024;21(1):189. 10.1186/s12984-024-01475-5 Aguirre-Ollinger G, Chua KS, Ong PL, Kuah CW, Plunkett TK, Ng CY, et al. Telerehabilitation using a 2-D planar arm rehabilitation robot for hemiparetic stroke: a feasibility study of clinic-to-home exergaming therapy. J Neuroeng Rehabil. 2024;21(1):207. 10.1186/s12984-024-01502-5 Huber SK, Manser P, de Bruin ED. PEMOCS: theory derivation of a concept for PErsonalized MOtor-Cognitive exergame training in chronic Stroke—a methodological paper with an application example. Front Sports Act Living. 2024;6:1397949. 10.3389/fspor.2024.1397949 Huber SK, Knols RH, Held JP, Betschart M, de Bruin ED. PEMOCS: Evaluating the effects of a concept-guided, PErsonalised, MOtor-Cognitive exergame training on cognitive functions and gait in chronic Stroke—study protocol for a randomised controlled trial. Trials. 2024;25(1):451. 10.1186/s13063-024-08289-2 Lorenz EA, Bråten Støen A, Lie Fridheim M, Alsos OA. Design recommendations for XR-based motor rehabilitation exergames at home. Front Virtual Real. 2024;5:1340072. 10.3389/frvir.2024.1340072 Liu M, Shen X, Pan W. Deep reinforcement learning for personalized treatment recommendation. Stat Med. 2022;41(20):4034-56. 10.1002/sim.9473 Gönül S, Namlı T, Coşar A, Toroslu İH. A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions. Artif Intell Med. 2021;115:102062. 10.1016/j.artmed.2021.102062 Luo S, Androwis G, Adamovich S, Nunez E, Su H, Zhou X, et al. Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning. J Neuroeng Rehabil. 2023;20(1):34. 10.1186/s12984-023-01157-8 Luo S, Androwis G, Adamovich S, Su H, Nunez E, Zhou X. Reinforcement learning and control of a lower extremity exoskeleton for squat assistance. Front Robot AI. 2021;8:702845. 10.3389/frobt.2021.702845 Han JI, Lee JH, Choi HS, Kim JH, Choi J. Policy design for an ankle-foot orthosis using simulated physical human-robot interaction via deep reinforcement learning. IEEE Trans Neural Syst Rehabil Eng. 2022;30:2186-97. 10.1109/TNSRE.2022.3195657 Li A, Minto R, Dölling M, Boschetti G, Zanotto D. Personalized adaptive assistance with reinforcement learning control enhances engagement, performance, and retention in robot-assisted arm-reaching exercises. IEEE Trans Neural Syst Rehabil Eng. 2025;34:532-42. 10.1109/TNSRE.2025.3528893 Khan NA, Jamwal PK, Hussain F, Ghayesh MH, Hussain S. Reinforcement learning-driven path generation for ankle rehabilitation robot using musculoskeletal-informed energy optimization. IEEE Trans Neural Syst Rehabil Eng. 2025;33:1-10. 10.1109/TNSRE.2025.3567891 Song S, Kidziński Ł, Peng XB, Ong C, Hicks J, Levine S, et al. Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation. J Neuroeng Rehabil. 2021;18(1):126. 10.1186/s12984-021-00912-2 De Vree L, Carloni R. Deep reinforcement learning for physics-based musculoskeletal simulations of healthy subjects and transfemoral prostheses’ users during normal walking. IEEE Trans Neural Syst Rehabil Eng. 2021;29:607-18. 10.1109/TNSRE.2021.3063301 Ye D, Luo H, Winstein C, Schweighofer N. Towards AI-based precision rehabilitation via contextual model-based reinforcement learning. J Neuroeng Rehabil. 2025;22(1):263. 10.1186/s12984-025-01622-8 Pelosi AD, Roth N, Yehoshua T, Itah D, Braun Benyamin O, Dahan A. Personalized rehabilitation approach for reaching movement using reinforcement learning. Sci Rep. 2024;14(1):17675. 10.1038/s41598-024-68479-z Arora S, Doshi P. A survey of inverse reinforcement learning: Challenges, methods and progress. Artif Intell. 2021;297:103500. 10.1016/j.artint.2021.103500 Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst. 2021;33(12):6999-7019. 10.1109/TNNLS.2021.3084827 Belogolovsky S, Korsunsky P, Mannor S, Tessler C, Zahavy T. Inverse reinforcement learning in contextual MDPs. Mach Learn. 2021;110(9):2295-334. 10.1007/s10994-021-06027-8 Bharadwaj VR, Miller BA, Novak VD, Jiang C. Learning skill training schedules from domain experts in a rehabilitation gym using inverse reinforcement learning. In: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). IEEE; 2024. p. 1815-21. 10.1109/BIOROB60548.2024.10601609 Luciani B, Pedrocchi A, Braghin F, Gandolla M. Learn from therapists’ demonstrations approaches for robotic rehabilitation exercises. In: International Conference on NeuroRehabilitation. Cham: Springer; 2024. p. 583-7. 10.1007/978-3-031-77589-8_92 Naranjo-Campos FJ, Victores JG, Balaguer C. Expert-trajectory-based features for apprenticeship learning via inverse reinforcement learning for robotic manipulation. Appl Sci. 2024;14(23):11131. 10.3390/app142311131 Liao Y, Vakanski A, Xian M. A deep learning framework for assessing physical rehabilitation exercises. IEEE Trans Neural Syst Rehabil Eng. 2020;28(2):468-77. 10.1109/TNSRE.2020.2966249 Peng XB, Abbeel P, Levine S, Van de Panne M. DeepMimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Trans Graph. 2018;37(4):1-14. 10.1145/3197517.3201311 Ozalp R, Ucar A, Guzelis C. Advancements in deep reinforcement learning and inverse reinforcement learning for robotic manipulation: toward trustworthy, interpretable, and explainable artificial intelligence. IEEE Access. 2024;12:51840-58. 10.1109/ACCESS.2024.3384457 Martínez-Pascual D, Catalán JM, Lledó LD, Blanco-Ivorra A, García-Aracil N. A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists’ demonstrations. J Neuroeng Rehabil. 2025;22(1):18. 10.1186/s12984-025-01501-2 Amiri Z, Sekhavat YA, Goljaryan S. StepAR: A personalized exergame for people with multiple sclerosis based on video-mapping. Entertain Comput. 2022;42:100487. 10.1016/j.entcom.2022.100487 Vallejo D, Gómez-Portes C, Albusac J, Glez-Morcillo C, Castro-Schez JJ. Personalized exergames language: a novel approach to the automatic generation of personalized exergames for stroke patients. Appl Sci. 2020;10(20):7378. 10.3390/app10207378 Mennella C, Maniscalco U, De Pietro G, Esposito M. A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions. Comput Biol Med. 2023;166:107485. 10.1016/j.compbiomed.2023.107485 van Doorn PF, Nyman E Jr, Wishaupt K, van der Krogt MM, Roerdink M. Deriving motor states and mobility metrics from gamified augmented reality rehabilitation exercises in people with Parkinson’s disease. Sensors. 2025;25(23):7172. 10.3390/s25237172