全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

DOI: 10.4236/abb.2024.155018, PP. 289-309

Keywords: Neuromuscular Diseases, Rehabilitation, Artificial Intelligence, Machine Learning, Robotic-Assisted Therapy, Virtual Reality, Personalized Treatment, Motor Function, Assistive Technologies, Algorithmic Rehabilitation

Full-Text   Cite this paper   Add to My Lib

Abstract:

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

References

[1]  Lusardi, M.M., Jorge, M. and Nielsen, C.C. (2012) Orthotics and Prosthetics in Rehabilitation-E-Book. Saunders, Philadelphia.
[2]  Proietti, T., Crocher, V., Roby-Brami, A. and Jarrasse, N. (2016) Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies. IEEE Reviews in Biomedical Engineering, 9, 4-14.
https://doi.org/10.1109/RBME.2016.2552201
[3]  Thring, M.W. (1983) Robots and Telechirs: Manipulators with Memory, Remote Manipulators, and Machine Limbs for the Handicapped (Ellis Horwood Series in Engineering Science). Halsted Press, Chichester.
[4]  Petrich, L.C. (2023) Advancing the Acceptance and Use of Wheelchair-Mounted Robotic Manipulators. Master’s Thesis, University of Alberta, Edmonton.
[5]  Demers, M., Rowe, J. and Prochazka, A. (2022) Passive Devices for Upper Limb Trainin. In: Reinkensmeyer, D.J., Marchal-Crespo, L. and Dietz, V., Eds., Neurorehabilitation Technology, Springer, Cham, 525-547.
https://doi.org/10.1007/978-3-031-08995-4_23
[6]  Santilli, V., Mangone, M., Diko, A., Alviti, F., Bernetti, A., Agostini, F., Palagi, L., Servidio, M., Paoloni, M., Goffredo, M. and Infarinato, F. (2023) The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients. International Journal of Environmental Research and Public Health, 20, Article 5575.
https://doi.org/10.3390/ijerph20085575
[7]  Chen, T. and Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 785-794.
https://doi.org/10.1145/2939672.2939785
[8]  Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.-Y. (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, 4-9 December 2017, 3149-3157.
[9]  Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. and Gulin, A. (2018) Catboost: Unbiased Boosting with Categorical Features. Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, 3-8 December 2018, 6638-6648.
[10]  Khan, J.I., Khan, J., Ali, F., Ullah, F., Bacha, J. and Lee, S. (2022) Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review. IEEE Access, 10, 62613-62660.
https://doi.org/10.1109/ACCESS.2022.3181605
[11]  Van der Loos, H.F., Reinkensmeyer, D.J. and Guglielmelli, E. (2016) Rehabilitation and Health Care Robotics. In: Siciliano, B. and Khatib, O., Eds., Springer Handbook of Robotics, Springer, Cham, 1685-1728.
https://doi.org/10.1007/978-3-319-32552-1_64
[12]  Heng, W., Solomon, S. and Gao, W. (2022) Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics. Advanced Materials, 34, Article 2107902.
https://doi.org/10.1002/adma.202107902
[13]  Liefaard, M.C., Lips, E.H., Wesseling, J., Hylton, N.M., Lou, B., Mansi, T. and Pusztai, L. (2021) The Way of the Future: Personalizing Treatment Plans through Technology. American Society of Clinical Oncology Educational Book, 41, 12-23.
https://doi.org/10.1200/EDBK_320593
[14]  He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T.M. and Mehnen, J. (2021) The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems. IEEE Transactions on Cognitive and Developmental Systems, 14, 1398-1412.
https://doi.org/10.1109/TCDS.2021.3132282
[15]  Shepherd, J., Procter, S. and Coley, I.L. (1996) Self-Care and Adaptations for Independent Living. In: Allen, A.S., Pratt, P.N. and Case-Smith, J., Eds., Occupational Therapy for Children, 3rd Edition, Mosby, St. Louis, 461-503.
[16]  Trombly, C.A. (2002) Managing Deficit of First Level Motor Control Capacities. In: Trombly, C.A. and Radomski, M.V., Eds., Occupational Therapy for Physical Dysfunction, 5th Edition, Lippincott Williams & Wilkins, Baltimore, 571-584.
[17]  Rahman, T., Basante, J. and Alexander, M. (2014) Robotics and Assistive Technology to Improve Function in Neuromuscular Diseases. Acta Mechanica Slovaca, 18, 50-57.
https://doi.org/10.21496/ams.2014.007
[18]  Singer, P.W. (2009) Wired for War: The Robotics Revolution and Conflict in the 21st Century. Penguin Books, London.
[19]  LeBlanc, M. and Leifer, L. (1982) Environmental Control and Robotic Manipulation Aids. IEEE Engineering in Medicine and Biology Magazine, 1, 16-22.
https://doi.org/10.1109/EMB-M.1982.5005839
[20]  Burgar, C.G., Lum, P.S., Shor, P.C., et al. (2000) Development of Robots for Rehabilitation Therapy: The Palo Alto VA/Stanford Experience. Journal of Rehabilitation Research and Development, 37, 863-873.
[21]  Krebs, H.I., Hogan, N., Aisen, M.L., et al. (1998) Robot-Aided Neurorehabilitation. IEEE Transactions on Rehabilitation Engineering, 6, 75-87.
https://doi.org/10.1109/86.662623
[22]  Fasoli, S.E., Fragala-Pinkham, M., Hughes, R., et al. (2008) Upper Limb Robotic Therapy for Children with Hemiplegia. American Journal of Physical Medicine & Rehabilitation, 87, 929-936.
https://doi.org/10.1097/PHM.0b013e31818a6aa4
[23]  Fasoli, S.E., Fragala-Pinkham, M., Hughes, R., et al. (2008) Robotic Therapy and Botulinum Toxin Type A: A Novel Intervention Approach for Cerebral Palsy. American Journal of Physical Medicine & Rehabilitation, 87, 1022-1025.
https://doi.org/10.1097/PHM.0b013e31817fb346
[24]  Cioi, D., Kale, A., Burdea, G., et al. (2011) Ankle Control and Strength Training for Children with Cerebral Palsy Using the Rutgers Ankle CP: A Case Study. 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, 29 June-1 July 2011, 1-6.
https://doi.org/10.1109/ICORR.2011.5975432
[25]  Assistive Innovations (2012) Assistive Innovations Product Listing.
https://www.assistive-innovations.com/
[26]  Rahman, T., Sample, W., Seliktar, R., et al. (2007) Design and Testing of a Functional Arm Orthosis in Patients with Neuromuscular Diseases. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15, 244-251.
https://doi.org/10.1109/TNSRE.2007.897026
[27]  Haumont, T., Rahman, T., Sample, W., et al. (2011) Wilmington Robotic Exoskeleton: A Novel Device to Maintain Arm Improvement in Muscular Disease. Journal of Pediatric Orthopaedics, 31, e44-e49.
https://doi.org/10.1097/BPO.0b013e31821f50b5
[28]  De Melo, W.C., Granger, E. and Lopez, M.B. (2021) MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal Depression Detection. IEEE Transactions on Affective Computing, 14, 578-590.
https://doi.org/10.1109/TAFFC.2021.3072579
[29]  Arai, H., Onga, Y., Ikuta, K., Chayama, Y., Iyatomi, H. and Oishi, K. (2021) Disease-Oriented Image Embedding with Pseudo-Scanner Standardization for Content-Based Image Retrieval on 3D Brain MRI. IEEE Access, 9, 165326-165340.
https://doi.org/10.1109/ACCESS.2021.3129105
[30]  Pinto, J.T., Brachkova, M.I., Fernandes, A.I. and Pinto, J.F. (2016) Evaluation of the Ability of Powdered Milk to Produce Minitablets Containing Paracetamol for the Paediatric Population. Chemical Engineering Research and Design, 110, 171-182.
https://doi.org/10.1016/j.cherd.2016.04.014
[31]  Yuan, Y. (2021) Observer Metamerism Quantification and Visualization. Master’s Thesis, Rochester Institute of Technology, Rochester.
[32]  Vos, T., et al. (2012) Years Lived with Disability (YLDs) for 1160 Sequelae of 289 Diseases and Injuries 1990-2010: A Systematic Analysis for the Global Burden of Disease Study 2010. The Lancet, 380, 2163-2196. (In English)
[33]  Hoy, D., Brooks, P., Blyth, F. and Buchbinder, R. (2010) The Epidemiology of Low Back Pain. Best Practice & Research Clinical Rheumatology, 24, 769-781. (In English)
https://doi.org/10.1016/j.berh.2010.10.002
[34]  Serranheira, F., Sousa-Uva, M., Heranz, F., Kovacs, F. and Sousa-Uva, A. (2020) Low Back Pain (LBP), Work and Absenteeism. Work, 65, 463-469.
https://doi.org/10.3233/WOR-203073
[35]  Karunanayake, A.L., Pathmeswaran, A., Kasturiratne, A. and Wijeyaratne, L.S. (2013) Risk Factors for Chronic Low Back Pain in a Sample of Suburban Sri Lankan Adult Males. International Journal of Rheumatic Diseases, 16, 203-210. (In English)
https://doi.org/10.1111/1756-185X.12060
[36]  Metgud, D.C., Khatri, S., Mokashi, M.G. and Saha, P.N. (2008) An Ergonomic Study of Women Workers in a Woolen Textile Factory for Identification of Health-Related Problems. Indian Journal of Occupational and Environmental Medicine, 12, 14-19. (In English)
https://doi.org/10.4103/0019-5278.40810
[37]  Bardin, L.D., King, P. and Maher, C.G. (2017) Diagnostic Triage for Low Back Pain: A Practical Approach for Primary Care. Medical Journal of Australia, 206, 268-273. (In English)
https://doi.org/10.5694/mja16.00828
[38]  Maher, C., Underwood, M. and Buchbinder, R. (2017) Non-Specific Low Back Pain. The Lancet, 389, 736-747. (In English)
https://doi.org/10.1016/S0140-6736(16)30970-9
[39]  Tack, C. (2019) Artificial Intelligence and Machine Learning Applications in Musculoskeletal Physiotherapy. Musculoskeletal Science & Practice, 39, 164-169. (In English)
https://doi.org/10.1016/j.msksp.2018.11.012
[40]  Amorim, P., Paulo, J.R., Silva, P.A., Peixoto, P., Castelo-Branco, M. and Martins, H. (2021) Machine Learning Applied to Low Back Pain Rehabilitation—A Systematic Review. International Journal of Digital Health, 1, 10.
https://doi.org/10.29337/ijdh.34
[41]  Tropea, P., et al. (2019) Rehabilitation, the Great Absentee of Virtual Coaching in Medical Care: Scoping Review. Journal of Medical Internet Research, 21, e12805. (In English)
https://doi.org/10.2196/12805
[42]  Fasoli, S.E., Krebs, H.I. and Hogan, N. (2004) Robotic Technology and Stroke Rehabilitation: Translating Research into Practice. Topics in Stroke Rehabilitation, 11, 11-19.
https://doi.org/10.1310/G8XB-VM23-1TK7-PWQU
[43]  Ai, Q., Liu, Z., Meng, W., Liu, Q. and Xie, S.Q. (2021) Machine Learning in Robot-Assisted Upper Limb Rehabilitation: A Focused Review. IEEE Transactions on Cognitive and Developmental Systems, 15, 2053-2063.
https://doi.org/10.1109/TCDS.2021.3098350
[44]  Veerbeek, J.M.V., Langbroek-Amersfoort, A.C.L., Van Wegen, E.E.V., Meskers, C.G.M. and Kwakkel, G.K. (2017) Effects of Robot-Assisted Therapy for the Upper Limb after Stroke: A Systematic Review and Meta-Analysis. Neurorehabilitation and Neural Repair, 31, 107-121.
https://doi.org/10.1177/1545968316666957
[45]  Mu, P., Dai, M. and Ma, X. (2021) Application of Artificial Intelligence in Rehabilitation Assessment. Journal of Physics: Conference Series, 1802, Article 032057.
[46]  Kaelin, V.C., Valizadeh, M., Salgado, Z., Parde, N. and Khetani, M.A. (2021) Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth with Disabilities: Scoping Review. Journal of Medical Internet Research, 23, e25745.
https://doi.org/10.2196/25745
[47]  Lo, K., Stephenson, M. and Lockwood, C. (2017) Effectiveness of Robotic Assisted Rehabilitation for Mobility and Functional Ability in Adult Stroke Patients: A Systematic Review. JBI Database of Systematic Reviews and Implementation Reports, 15, 3049-3091.
[48]  Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S.W., Palade, V. and Duncan, M.J. (2023) Artificial Intelligence for Skeleton-Based Physical Rehabilitation Action Evaluation: A Systematic Review. Computers in Biology and Medicine, 158, Article 106835.
https://doi.org/10.1016/j.compbiomed.2023.106835
[49]  Dobkin, B.H. and Dorsch, A. (2011) The Promise of mHealth: Daily Activity Monitoring and Outcome Assessments by Wearable Sensors. Neurorehabilitation and Neural Repair, 25, 788-798.
https://doi.org/10.1177/1545968311425908
[50]  Turchi, T., Prencipe, G., Malizia, A., Filogna, S., Latrofa, F. and Sgandurra, G. (2024) Pathways to Democratized Healthcare: Envisioning Human-Centered AI-as-a-Service for Customized Diagnosis and Rehabilitation. Artificial Intelligence in Medicine, 151, Article 102850.
https://doi.org/10.1016/j.artmed.2024.102850
[51]  Patel, U.K., Anwar, A., Saleem, S., Malik, P., Rasul, B., Patel, K., Yao, R., Seshadri, A., Yousufuddin, M. and Arumaithurai, K. (2021) Artificial Intelligence as an Emerging Technology in the Current Care of Neurological Disorders. Journal of Neurology, 268, 1623-1642.
https://doi.org/10.1007/s00415-019-09518-3
[52]  Jozdani, S.E., Johnson, B.A. and Chen, D. (2019) Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing, 11, Article 1713.
https://doi.org/10.3390/rs11141713
[53]  Rahman, S., Sarker, S., Haque, A.K.M.N., Uttsha, M.M., Islam, F. and Deb, S. (2022) AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 192-207.
https://doi.org/10.1109/TNSRE.2022.3219085
[54]  Di Pino, G., Pellegrino, G., Assenza, G., Capone, F., Ferreri, F., Formica, D., Ranieri, F., Tombini, M., Ziemann, U., Rothwell, J.C. and Di Lazzaro, V. (2014) Modulation of Brain Plasticity in Stroke: A Novel Model for Neurorehabilitation. Nature Reviews Neurology, 10, 597-608.
https://doi.org/10.1038/nrneurol.2014.162
[55]  Laver, K.E., George, S., Thomas, S., Deutsch, J.E. and Crotty, M. (2015) Virtual Reality for Stroke Rehabilitation. Cochrane Database of Systematic Reviews, No. 2, Article No. CD008349.
https://doi.org/10.1002/14651858.CD008349.pub3
[56]  Tak, S., Choi, W. and Lee, S. (2015) Game-Based Virtual Reality Training Improves Sitting Balance after Spinal Cord Injury: A Single-Blinded, Randomized Controlled Trial. Medical Science & Technology, 56, 53-59.
[57]  Dos Santos, L.F., Christ, O., Mate, K., Schmidt, H., Krüger, J. and Dohle, C (2016) Movement Visualisation in Virtual Reality Rehabilitation of the Lower Limb: A Systematic Review. BioMedical Engineering OnLine, 15, Article No. 144.
https://doi.org/10.1186/s12938-016-0289-4
[58]  Lee, S.H., Lee, J.Y., Kim, M.Y., Jeon, Y.J., Kim, S. and Shin, J.H. (2018) Virtual Reality Rehabilitation with Functional Electrical Stimulation Improves Upper Extremity Function in Patients with Chronic Stroke: A Pilot Randomized Controlled Study. Archives of Physical Medicine and Rehabilitation, 99, 1447-1453.
https://doi.org/10.1016/j.apmr.2018.01.030
[59]  Borstad, A.L., Crawfis, R., Phillips, K., Lowes, L.P., Maung, D., McPherson, R., Siles, A., Worthen-Chaudhari, L. and Gauthier, L.V. (2018) In-Home Delivery of Constraint-Induced Movement Therapy via Virtual Reality Gaming. Journal of Patient-Centered Research and Reviews, 5, 6-17.
https://doi.org/10.17294/2330-0698.1550
[60]  Gallagher, J.F., Sivan, M. and Levesley, M. (2022) Making Best Use of Home-Based Rehabilitation Robots. Applied Sciences, 12, Article 1996.
https://doi.org/10.3390/app12041996
[61]  Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Hassan, M. and Seneviratne, A. (2017) A Survey of Wearable Devices and Challenges. IEEE Communications Surveys & Tutorials, 19, 2573-2620.
https://doi.org/10.1109/COMST.2017.2731979
[62]  Gull, M.A., Bai, S. and Bak, T. (2020) A Review on Design of Upper Limb Exoskeletons. Robotics, 9, Article 16.
https://doi.org/10.3390/robotics9010016
[63]  Cunha, B., Ferreira, R. and Sousa, A.S.P. (2023) Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview. Sensors, 23, Article 7100.
https://doi.org/10.3390/s23167100

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133