Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine (BG), Italy.
Azienda Socio Sanitaria Territoriale (ASST) Papa Giovanni XXIII, 24127 Bergamo, Italy.
Sensors (Basel). 2020 Nov 4;20(21):6273. doi: 10.3390/s20216273.
In physical rehabilitation, motion capture solutions are well-known but not as widespread as they could be. The main limit to their diffusion is not related to cost or usability but to the fact that the data generated when tracking a person must be elaborated according to the specific context and aim. This paper proposes a solution including customized motion capture and data elaboration with the aim of supporting medical personnel in the assessment of spinal cord-injured (SCI) patients using a wheelchair. The configuration of the full-body motion capturing system is based on an asymmetric 3 Microsoft Kinect v2 sensor layout that provides a path of up to 6 m, which is required to properly track the wheelchair. Data elaboration is focused on the automatic recognition of the pushing cycles and on plotting any kinematic parameter that may be interesting in the assessment. Five movements have been considered to evaluate the wheelchair propulsion: the humeral elevation, the horizontal abduction of the humerus, the humeral rotation, the elbow flexion and the trunk extension along the sagittal plane. More than 60 volunteers with a spinal cord injury were enrolled for testing the solution. To evaluate the reliability of the data computed with SCI APPlication (APP) for the pushing cycle analysis, the patients were subdivided in four groups according to the level of the spinal cord injury (i.e., high paraplegia, low paraplegia, C7 tetraplegia and C6 tetraplegia). For each group, the average value and the standard deviation were computed and a comparison with similar acquisitions performed with a high-end solution is shown. The measurements computed by the SCI-APP show a good reliability for analyzing the movements of SCI patients' propulsion wheelchair.
在物理康复中，运动捕捉解决方案是众所周知的，但并没有得到广泛应用。它们普及的主要限制不是成本或可用性，而是所跟踪人员的数据必须根据具体情况和目标进行精心处理。本文提出了一种解决方案，包括定制的运动捕捉和数据处理，旨在支持医务人员使用轮椅评估脊髓损伤（SCI）患者。全身运动捕捉系统的配置基于非对称的 3 个 Microsoft Kinect v2 传感器布局，提供了长达 6 米的路径，这是正确跟踪轮椅所需的。数据处理主要集中在自动识别推动周期和绘制评估中可能感兴趣的任何运动学参数上。已经考虑了五个运动来评估轮椅推进：肱骨抬高、肱骨水平外展、肱骨旋转、肘部弯曲和沿矢状面伸展躯干。超过 60 名脊髓损伤志愿者参加了该解决方案的测试。为了评估 SCI APPlication（APP）计算的数据在推动周期分析中的可靠性，根据脊髓损伤水平将患者分为四组（即高位截瘫、低位截瘫、C7 四肢瘫痪和 C6 四肢瘫痪）。对于每组，计算平均值和标准差，并与使用高端解决方案进行的类似采集进行比较。由 SCI-APP 计算的测量值对于分析 SCI 患者推动轮椅的运动具有良好的可靠性。
