MOTOR NEUROREHABILITATION IN PATIENTS WITH HEMIPLEGIA
Keywords:
Robotic therapy, Robotic assisted exoskeleton, motor function, lower extremitiesAbstract
In recent decades, progress has been observed in motor rehabilitation interventions, based on repetitive
practice of coordinated motor activities that are efficient and aim to improve movement functions, resulting in an
improvement in quality of life of the patients. Robotic rehabilitation is a computer-software connected orthosis,
which focuses on performing certain coordinated movements, aimed at restoring damaged sensory, motor and
cognitive skills. Robotic rehabilitation is experiencing its rise by attaching sensors for the purpose of multimedia
sensing, and the most applied and used are visual and auditory sensors. Visual feedback is an important factor in
human-robot interaction, one of the most widely used open feedback models is visual feedback. Visual feedback is
delivered through an additional computer screen, its purpose is cognitive training. The visual part of the training is
closely correlated with the auditory information that makes the computerized rehabilitation unique. Combining
cognitive training with the help of robots and together with virtual reality techniques offers us a new and significant
effective alternative to the traditional way of training, gamification (the use of games with robotic rehabilitation) is
becoming a more popular way of motivating in cognitive training. The incorporation of virtual reality allows us to
repeat tasks, exercises, movement in a more comfortable and convenient way of motor rehabilitation. Thr aim of this
study is to presents a unique case with hemiplegia where, in addition to all kinesitherapy and a physical plan, the
rehabilitation includes robotic therapy of the lower extremities, where a robot-assisted exoskeleton (locomat) is
used. Materials and methods: The robotic therapy together with the purpose of rehabilitation treatment takes place in
a period of twenty days with a break on the 10th day of a week, in order to show the progress the patient has made
between the two treatments of 10 days each and to determine how much the robotic therapy has an effect on motor
neurorehabilitation in patients with hemiplegia. In order to determine the independence and mobility of the patient at
the beginning, on the 10th day and on the 20th day, the Barthel index test and the Fugl-Meyer assessment test were
performed. Results: According to the analysis and processing of the results obtained from the robot-assisted
exoskeleton itself and after their statistical processing, they show a significant improvement in the results in the
second period of rehabilitation compared to the first rehabilitation, which means that the patient took a significant
part in the movement itself with the robot-assisted exoskeleton and a significant patient improvement in terms of
walking distance, meters walked, treadmill speed, driving force and body weight support. The overall result also
results in an improvement in coordination and the establishment of a straight pattern of walking, which only hinders
further rehabilitation. The results of the Bartel index test shows the progress of the patient and the improvement of
his mobility, while the Fugl-Meyer assessment test shows the improvement of motor function and sensitivity.
Conclusion: The robotic tribulation, although it is still being developed and because of its inaccessibility in relation
to the price of tretmans, shows a significant improvement in motor function and in motor neurorehabilitation in
patients with hemiplegia.
References
Carpaneto, J., & Micera, S. (2015). Application of orthoses and neurostimulation in neurorehabilitation. Oxford University Press.
Choi, K.-S., Kwon, I.-H., & Shin, W.-S. (2023). Comparison of effects on static balance in stroke patients according to visual biofeedback methods. Physical Therapy Rehabilitation Science, 12(3), 320–326. https://doi.org/10.14474/ptrs.2023.12.3.320
Di Tommaso, F., Tamburella, F., Lorusso, M., Gastaldi, L., Molinari, M., & Tagliamonte, N. L. (2023). Biomechanics of exoskeleton-assisted treadmill walking. IEEE ... International Conference on Rehabilitation Robotics, 2023, 1–6. https://doi.org/10.1109/ICORR58425.2023.10304685
Dolgirev, A., & Maltseva, N. (2020). Development of the legs fixation mechanism for Lokomat therapy training device. In Advanced Problems in Mechanics (pp. 81–92). Springer International Publishing.
Esquenazi, A., & Talaty, M. (2019). Robotics for lower limb rehabilitation. Physical Medicine and Rehabilitation Clinics of North America, 30(2), 385–397. https://doi.org/10.1016/j.pmr.2018.12.012
Kawahira, K., Shimodozono, M., & Noma, T. (Eds.). (2023). Exercise therapy for recovery from hemiplegia: Theory and practice of repetitive facilitative exercise (1st ed.). Springer.
Kernel Networks Inc. (2019). Innovative biofeedback interface for enhancing stroke gait rehabilitation. Case Medical Research. https://doi.org/10.31525/ct1-nct04013971
Krakauer, J. W. (2015). The applicability of motor learning to neurorehabilitation. Oxford University Press.
Lünenburger, L., Colombo, G., Riener, R., & Dietz, V. (2004). Biofeedback in gait training with the robotic orthosis Lokomat. Conference Proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2004, 4888–4891. https://doi.org/10.1109/IEMBS.2004.1404352
Marquez-Chin, C., Kapadia-Desai, N., & Kalsi-Ryan, S. (2021). Brain–computer interfaces: Neurorehabilitation of voluntary movement after stroke and spinal cord injury. Morgan & Claypool.
Masengo, G., Zhang, X., Dong, R., Alhassan, A. B., Hamza, K., & Mudaheranwa, E. (2022). Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research. Frontiers in Neurorobotics, 16, 913748. https://doi.org/10.3389/fnbot.2022.913748
Mehrabi, N., McPhee, J., Shourijeh, M. S., & Fregly, B. J. (Eds.). (2020). Advances in Musculoskeletal Modeling and their Application to Neurorehabilitation. Frontiers Media SA.
Peng, Z., Luo, R., Huang, R., Yu, T., Hu, J., Shi, K., & Cheng, H. (2020). Data-driven optimal assistance control of a lower limb exoskeleton for hemiplegic patients. Frontiers in Neurorobotics, 14, 37. https://doi.org/10.3389/fnbot.2020.00037
Rangel, L. (Ed.). (2023). Current progress in neurorehabilitation. Hayle Medical.
Reinkensmeyer, D. J., Marchal-Crespo, L., & Dietz, V. (Eds.). (2023). Neurorehabilitation technology (3rd ed.). Springer International Publishing.
Smania, N., Tamburin, S., Sandrini, G., Saltuari, L., & Hoemberg, V. (Eds.). (2019). New Advances in Neurorehabilitation. Frontiers Media SA.
Yang, J., Zhu, Y., Li, H., Wang, K., Li, D., & Qi, Q. (2024). Effect of robotic exoskeleton training on lower limb function, activity and participation in stroke patients: a systematic review and meta-analysis of randomized controlled trials. Frontiers in Neurology, 15. https://doi.org/10.3389/fneur.2024.1453781
Zhang, Y., Zhao, W., Wan, C., Wu, X., Huang, J., Wang, X., Huang, G., Ding, W., Chen, Y., Yang, J., Su, B., Xu, Y., Zhou, Z., Zhang, X., Miao, F., Li, J., & Li, Y. (2024). Exoskeleton rehabilitation robot training for balance and lower limb function in sub-acute stroke patients: a pilot, randomized controlled trial. Journal of Neuroengineering and Rehabilitation, 21(1), 98. https://doi.org/10.1186/s12984-024-01391-0
