Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry

Abstract

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.

Keywords: Disabilities; Dynamic inertial model; Model scaling; Motion capture technology; Motor deficits; Movement analysis; Movement redundancy; Quantitative data; Rehabilitation; Remote healthcare; Upper extremity movements; Virtual reality.

Figure 1.

Illustration of the effect of 10% change in segment lengths on the resulting arm pose. Red segments are 10% shorter each than the black segments. Dashed lines show axes against the joint angles are measured. E/F stands for extension/flexion degree of freedom of the corresponding joint. A. When the joint angles are kept the same, the changes in limb segment lengths change the location where the hand can reach. B. When the tip of the hand is kept the same, the changes in limb segment lengths change joint angles.

Figure 2.

Example of the differences between joint angle trajectories calculated with models scaled in two ways for a single movement in one direction (and back) performed by one participant. Joint angle trajectories were calculated from musculoskeletal models scaled using average proportions (black) and individual arm segment lengths (red). Thick lines show average trajectories and shaded areas show standard deviation across 15 repetitions for the same movement. Abbreviations: E/F = extension/flexion; Ab/Ad = abduction/adduction; E/I = external/internal; R/U = radial/ulnar.

Figure 3.

Root mean squared error (RMSE) between joint angles calculated using the two scaling methods. RMSE was averaged across all subjects for each target location and degree of freedom. Thick black lines show median RMSE values, shaded areas show 2dn and 3rd quartile ranges, whiskers show maximal ranges of RMSE values. Red lines show the average inter-trial standard deviation for corresponding movements and degrees of freedom. The insert shows the locations of targets 1–14 relative to the subject’s position (drawn not to scale). Abbreviations: E/F = extension/flexion; Ab/Ad = abduction/adduction; E/I = external/internal.