Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran.
Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran.
Comput Biol Med. 2024 Sep;179:108875. doi: 10.1016/j.compbiomed.2024.108875. Epub 2024 Jul 16.
While motor recovery is preferred to compensatory movements for stroke patients with mild to moderate motion impairment, current movement quality assessments rarely reflect the differences between a patient's pre- and post-stroke movement patterns. Such comparison can help therapists to identify the rate of the restoration of premorbid motion patterns and prescribe the most effective treatment.
This paper attempted to present a new biomechanical metric for the quality of upper-limb movements which uses the subject's optimal movements as a reference to evaluate his/her UL movement quality. To this end, an inverse optimal control algorithm was applied to find an estimation of the patient's premorbid motion patterns. The new biomechanical index was then calculated as a measure of similarity between the optimal and actual movement trajectories. In the next part, various simulation and clinimetric investigations were performed to evaluate the responses of the new index to variations of the movement quality as well as its test-retest reliability and concurrent validity.
Simulation-based analyses demonstrated that the proposed index, in contrast to the previous popular biomechanical indices, can successfully detect a wide range of abnormalities in motion signals. In addition, it showed good test-retest reliability (ICC = 0.89) and moderate correlation with clinical indices, Fugl-Meyer Assessment (r = 0.66), Action Research Arm Test (r = 0.47), and ABILHAND (r = 0.27).
Although the proposed index has the same degree of clinimetric properties as the previous metrics, the ability to identify the level of movement restoration and also various types and severities of motor disabilities may lead to better design and management of motor rehabilitation.
对于运动功能损伤轻至中度的脑卒中患者，更倾向于进行运动功能恢复治疗，而不是代偿运动。然而，目前的运动质量评估很少反映患者卒中前后运动模式的差异。这种比较可以帮助治疗师识别出患者运动模式恢复的速度，并制定最有效的治疗方案。
本文尝试提出一种新的上肢运动质量的生物力学指标，该指标使用受试者的最佳运动作为参考，来评估其上肢运动质量。为此，应用逆最优控制算法来寻找患者发病前运动模式的估计值。然后，将新的生物力学指标计算为最佳和实际运动轨迹之间相似性的度量。在接下来的部分中，进行了各种模拟和临床研究，以评估新指标对运动质量变化的响应，以及其重测信度和同时效度。
基于模拟的分析表明，与以前流行的生物力学指标相比，所提出的指标能够成功地检测到运动信号的广泛异常。此外，它还表现出良好的重测信度（ICC=0.89），与临床指标（Fugl-Meyer 评估，r=0.66；行动研究上肢测试，r=0.47；ABILHAND，r=0.27）具有中度相关性。
虽然所提出的指标与以前的指标具有相同程度的临床计量特性，但它能够识别运动恢复的水平，以及各种类型和严重程度的运动障碍，可能会导致更好的运动康复设计和管理。
