Ilies Alexandru Bogdan, Cheregi Cornel, Thowayeb Hassan Hassan, Wendt Jan Reinald, Horgos Maur Sebastian, Lazar Liviu
Faculty of Medicine and Pharmacy, University of Oradea, Str. Piata 1 Decembrie nr. 10, 410087 Oradea, Romania.
Social Studies Department, King Faisal University, Hofuf 31982, Saudi Arabia.
Bioengineering (Basel). 2025 Jul 10;12(7):752. doi: 10.3390/bioengineering12070752.
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.
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 -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.
No statistically significant asymmetries were found between left and right limb measurements, indicating functional symmetry post-treatment ( > 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 ( < 0.01), explaining up to 60% of the variance in outcomes.
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.
Lokomat辅助机器人康复越来越多地用于脊髓损伤（SCI）患者的步态恢复。然而，通过生物力学参数和机器学习方法对治疗效果进行客观评估仍未得到充分探索。
本研究分析了29例接受基于Lokomat康复治疗的SCI患者的数据。使用统计方法（配对t检验、方差分析和普通最小二乘法回归）、聚类技术（k均值）、降维（t-SNE）和异常检测（孤立森林）对包含运动范围（L-ROM）、关节刚度（L-STIFF）和肌肉力量（L-FORCE）在内的46个变量的数据集进行了检查。应用预测模型评估年龄、速度、体重、体重支持和运动持续时间对生物力学结果的影响。
左右肢体测量之间未发现统计学上的显著不对称，表明治疗后功能对称（P>0.05）。聚类分析显示患者组之间结构较弱（轮廓系数≈0.31）。孤立森林在刚度数据中识别出极少的异常，支持治疗的一致性。回归模型表明，体重和体重支持对关节刚度有显著影响（P<0.01），可解释高达60%的结果方差。
Lokomat辅助机器人康复在SCI患者中显示出高度的功能对称性和生物力学一致性。机器学习方法为结果的结构和可预测性提供了有意义的见解，突出了体重和支持参数在制定恢复方案中的临床价值。
