Orthopaedic and Rehabilitation Engineering Center (OREC), Marquette University, Olin Engineering Suite 323, Milwaukee, WI, 53201-1881, USA.
Department of Biomedical Engineering, Marquette University, Olin Engineering Suite 323, Milwaukee, WI, 53201-1881, USA.
J Neuroeng Rehabil. 2018 Nov 6;15(1):96. doi: 10.1186/s12984-018-0444-1.
Wheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, with the ease of use of a standardized outcome assessment, would be optimal for repeated, longitudinal assessment of manual wheelchair users' therapeutic progress, but has yet to be offered.
This work evaluates a markerless motion analysis system for manual wheelchair mobility in clinical, community, and home settings. This system includes Microsoft® Kinect® 2.0 sensors, OpenSim musculoskeletal modeling, and an automated detection, processing, and training interface. The system is designed to be cost-effective, easily used by caregivers, and capable of detecting key kinematic metrics involved in manual wheelchair propulsion. The primary technical advancements in this research are the software components necessary to detect and process the upper extremity kinematics during manual wheelchair propulsion, along with integration of the components into a complete system. The study defines and evaluates an adaptable systems methodology for processing kinematic data using motion capture technology and open-source musculoskeletal models to assess wheelchair propulsion pattern and biomechanics, and characterizes its accuracy, sensitivity and repeatability. Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significantly correlated (p < 0.05).
The system is recommended for use in clinical settings for frequent wheelchair propulsion assessment, provided the limitations in precision are considered. The motion capture-model software bridge methodology could be applied in the future to any motion-capture system or specific application, broadening access to detailed kinematics while reducing assessment time and cost.
轮椅生物力学研究提高了手动轮椅使用者的可及性和临床护理水平。标准化的结果评估是跟踪进展的重要工具，但我们非常需要更多的定量方法。如果有一种系统能够提供运动学、定量检测，同时又具有标准化结果评估的易用性，那么它将非常适合对手动轮椅使用者治疗进展进行重复、纵向评估，但目前还没有这样的系统。
本研究评估了一种用于临床、社区和家庭环境中手动轮椅移动的无标记运动分析系统。该系统包括 Microsoft® Kinect® 2.0 传感器、OpenSim 肌肉骨骼建模以及自动检测、处理和培训接口。该系统旨在具有成本效益，易于护理人员使用，并能够检测到手动轮椅推进中涉及的关键运动学指标。本研究的主要技术进步是检测和处理手动轮椅推进过程中上肢运动学所需的软件组件，以及将这些组件集成到一个完整的系统中。该研究定义并评估了一种使用运动捕捉技术和开源肌肉骨骼模型处理运动学数据的适应性系统方法，以评估轮椅推进模式和生物力学，并对其准确性、灵敏度和可重复性进行了特征描述。时空参数、关节活动范围和肌肉肌腱运动的试验间重复性均高度相关（p < 0.05）。
该系统建议在临床环境中用于频繁的轮椅推进评估，只要考虑到精度的限制。运动捕捉-模型软件桥接方法将来可以应用于任何运动捕捉系统或特定应用，在减少评估时间和成本的同时，扩大对详细运动学的访问。
Zotero 插件
