Characterization of normative angular joint kinematics during two functional upper limb tasks.

Abstract

BACKGROUND\nOptical motion capture is a powerful tool for assessing upper body kinematics, including compensatory movements, in different populations. However, the lack of a standardized protocol with clear functional relevance hinders its clinical acceptance.\n\n\nRESEARCH QUESTION\nThe objective of this study was to use motion capture to: (1) characterize angular joint kinematics in a normative population performing two complex, yet standardized upper limb tasks with clear functional relevance; and (2) assess the protocol s intra-rater reliability.\n\n\nMETHODS\nTwenty non-disabled adults performed the previously developed Pasta Box Task and Cup Transfer Task. The kinematics of the upper body were captured using an optoelectronic motion capture system and rigid plates with reflective markers. Angular joint trajectories, peak angle, range of motion (RoM), and peak angular velocity were extracted for the trunk, shoulder, elbow, forearm, and wrist. Intra-class correlation was used to assess the intra-rater reliability of the kinematic measures.\n\n\nRESULTS\nBoth tasks required minimal trunk motion. Cross-body movements required greater RoM at the trunk, shoulder, and elbow joints compared to movements in front of the body. Reaches to objects further away from the body required greater trunk and elbow joint RoM compared to reaches to objects closer to the body. Transporting the box of pasta required the wrist to maintain an extended position. The two different grasp patterns in the Cup Transfer Task forced the wrist into a flexed and ulnar-deviated position for the near cup, and an extended and radial-deviated position for the far cup. For both tasks, the majority of measures displayed intra-class correlation values above 0.75, indicating good reliability.\n\n\nSIGNIFICANCE\nOur protocol and functional tasks elicit a degree of movement sensitivity that is not available in current clinical assessments. Our study also provides a comprehensive dataset that can serve as a normative benchmark for quantifying movement compensations following impairment.