Taitano Rachel I, Gritsenko Valeriya
Department of Neuroscience, School of Medicine, West Virginia University, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, USA.
Department of Human Performance, Division of Physical Therapy, School of Medicine, West Virginia University, Department of Neuroscience, School of Medicine, West Virginia University, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, USA.
bioRxiv. 2024 Sep 7:2024.09.03.611088. doi: 10.1101/2024.09.03.611088.
Movement analysis is a critical tool in understanding and addressing various disabilities associated with movement deficits. By analyzing movement patterns, healthcare professionals can identify the root causes of these alterations, which is essential for preventing, diagnosing, and rehabilitating a broad spectrum of medical conditions, disabilities, and injuries. With the advent of affordable motion capture technologies, quantitative data on patient movement is more accessible to clinicians, enhancing the quality of care. Nonetheless, it is crucial that these technologies undergo rigorous validation to ensure their accuracy in collecting and monitoring patient movements, particularly for remote healthcare services where direct patient observation is not possible. In this study, motion capture technology was used to track upper extremity movements during a reaching task presented in virtual reality. Kinematic data was then calculated for each participant using a scaled dynamic inertial model. The goal was to evaluate the accuracy of joint angle calculations using inverse kinematics from motion capture relative to the typical movement redundancy. Shoulder, elbow, radioulnar, and wrist joint angles were calculated with models scaled using either direct measurements of each individual's arm segment lengths or those lengths were calculated from individual height using published average proportions. The errors in joint angle trajectories calculated using the two methods of model scaling were compared to the inter-trial variability of those trajectories. The variance of this error was primarily within the normal range of variability between repetitions of the same movements. This suggests that arm joint angles can be inferred with good enough accuracy from motion capture data and individual height to be useful for the clinical assessment of motor deficits.
运动分析是理解和解决与运动功能障碍相关的各种残疾问题的关键工具。通过分析运动模式，医疗保健专业人员可以确定这些变化的根本原因，这对于预防、诊断和康复广泛的医疗状况、残疾和损伤至关重要。随着经济实惠的运动捕捉技术的出现，临床医生更容易获得关于患者运动的定量数据，从而提高了护理质量。尽管如此，至关重要的是，这些技术要经过严格验证，以确保其在收集和监测患者运动方面的准确性，特别是对于无法直接观察患者的远程医疗服务。在本研究中，使用运动捕捉技术来跟踪虚拟现实中呈现的伸手任务期间的上肢运动。然后使用缩放动态惯性模型为每个参与者计算运动学数据。目标是评估使用运动捕捉的逆运动学相对于典型运动冗余度来计算关节角度的准确性。使用个体手臂节段长度的直接测量值或根据公布的平均比例从个体身高计算出的长度来缩放模型，计算肩部、肘部、桡尺关节和腕关节角度。将使用两种模型缩放方法计算的关节角度轨迹误差与这些轨迹的试验间变异性进行比较。该误差的方差主要在相同运动重复之间的正常变异性范围内。这表明，可以从运动捕捉数据和个体身高以足够高的准确性推断手臂关节角度，从而有助于对运动功能障碍进行临床评估。
