Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors
Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This...
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MDPI AG
2025-07-01
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| author | Alexandru Bogdan Ilies Cornel Cheregi Hassan Hassan Thowayeb Jan Reinald Wendt Maur Sebastian Horgos Liviu Lazar |
| author_facet | Alexandru Bogdan Ilies Cornel Cheregi Hassan Hassan Thowayeb Jan Reinald Wendt Maur Sebastian Horgos Liviu Lazar |
| author_sort | Alexandru Bogdan Ilies |
| collection | DOAJ |
| description | Background: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired <i>t</i>-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (<i>p</i> > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (<i>p</i> < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-bef831cf41be4ac7a1bcc880554f0ec52025-08-20T02:45:37ZengMDPI AGBioengineering2306-53542025-07-0112775210.3390/bioengineering12070752Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive FactorsAlexandru Bogdan Ilies0Cornel Cheregi1Hassan Hassan Thowayeb2Jan Reinald Wendt3Maur Sebastian Horgos4Liviu Lazar5Faculty of Medicine and Pharmacy, University of Oradea, Str. Piata 1 Decembrie nr. 10, 410087 Oradea, RomaniaFaculty of Medicine and Pharmacy, University of Oradea, Str. Piata 1 Decembrie nr. 10, 410087 Oradea, RomaniaSocial Studies Department, King Faisal University, Hofuf 31982, Saudi ArabiaSzpital Św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 Gdynia, PolandFaculty of Medicine and Pharmacy, University of Oradea, Str. Piata 1 Decembrie nr. 10, 410087 Oradea, RomaniaFaculty of Medicine and Pharmacy, University of Oradea, Str. Piata 1 Decembrie nr. 10, 410087 Oradea, RomaniaBackground: Lokomat-assisted robotic rehabilitation is increasingly used for gait restoration in patients with spinal cord injury (SCI). However, the objective evaluation of treatment effectiveness through biomechanical parameters and machine learning approaches remains underexplored. Methods: This study analyzed data from 29 SCI patients undergoing Lokomat-based rehabilitation. A dataset of 46 variables including range of motion (L-ROM), joint stiffness (L-STIFF), and muscular force (L-FORCE) was examined using statistical methods (paired <i>t</i>-test, ANOVA, and ordinary least squares regression), clustering techniques (k-means), dimensionality reduction (t-SNE), and anomaly detection (Isolation Forest). Predictive modeling was applied to assess the influence of age, speed, body weight, body weight support, and exercise duration on biomechanical outcomes. Results: No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment (<i>p</i> > 0.05). Clustering analysis revealed a weak structure among patient groups (Silhouette score ≈ 0.31). Isolation Forest identified minimal anomalies in stiffness data, supporting treatment consistency. Regression models showed that body weight and body weight support significantly influenced joint stiffness (<i>p</i> < 0.01), explaining up to 60% of the variance in outcomes. Conclusions: Lokomat-assisted robotic rehabilitation demonstrates high functional symmetry and biomechanical consistency in SCI patients. Machine learning methods provided meaningful insight into the structure and predictability of outcomes, highlighting the clinical value of weight and support parameters in tailoring recovery protocols.https://www.mdpi.com/2306-5354/12/7/752spinal cord injuryLokomatrobotic rehabilitationbiomechanical symmetrymachine learninggait training |
| spellingShingle | Alexandru Bogdan Ilies Cornel Cheregi Hassan Hassan Thowayeb Jan Reinald Wendt Maur Sebastian Horgos Liviu Lazar Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors Bioengineering spinal cord injury Lokomat robotic rehabilitation biomechanical symmetry machine learning gait training |
| title | Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors |
| title_full | Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors |
| title_fullStr | Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors |
| title_full_unstemmed | Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors |
| title_short | Lokomat-Assisted Robotic Rehabilitation in Spinal Cord Injury: A Biomechanical and Machine Learning Evaluation of Functional Symmetry and Predictive Factors |
| title_sort | lokomat assisted robotic rehabilitation in spinal cord injury a biomechanical and machine learning evaluation of functional symmetry and predictive factors |
| topic | spinal cord injury Lokomat robotic rehabilitation biomechanical symmetry machine learning gait training |
| url | https://www.mdpi.com/2306-5354/12/7/752 |
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