Feasibility of the Meta Quest 3 for upper limb kinematic analysis: accuracy, consistency, agreement and reliability

Upper limb motor impairment is a frequent long-term consequence of a wide range of neurological and musculoskeletal conditions. Accurate assessment of motor function is essential to determine patient’s condition and plan adequate interventions. While clinical scales are easy to administer and not time-consuming, they could have limited accuracy and be biased. Kinematic analysis can provide detailed, quantitative information but usually depends on expensive, complex, and non-portable systems. Recent virtual reality head-mounted displays, with integrated hand-tracking, offer a promising, low-cost, and portable alternative to conduct upper limb kinematic assessment. However, the accuracy, consistency, agreement and reliability of kinematic variables derived from the hand tracking feature of these devices, as well as the impact of signal preprocessing on feature extraction, remain largely unexplored.

Thirty-nine healthy subjects performed five upper-limb tasks including tracking, point-reaching, and reach-and-grasp, while their movements were simultaneously recorded using a laboratory-grade motion capture system and the Meta Quest 3 headset. Multiple kinematic variables were extracted from the hand trajectories of both systems and analysed to evaluate accuracy, consistency, agreement and reliability. Additionally, the impact of signal smoothing on the extracted kinematic values was examined.

Minimal absolute and relative errors were estimated between systems, showing high concordance and consistency across most kinematic variables and tasks. Importantly, the differences observed remained below the minimal detectable change of the motion capture system. Test-retest reliability of kinematic measures estimated by the Meta Quest 3 was moderate to good for nearly all variables with the only exception of the number of movement units, which consistently showed poor reliability across all tasks, indicating limited robustness of this metric. The signal smoothing was found to influence the magnitude of the estimated errors, highlighting the importance of optimizing the preprocessing to maximize the accuracy of the Meta Quest 3.

Our findings support the feasibility of using the Meta Quest 3 as a cost-effective and portable alternative to laboratory-grade motion capture systems for kinematic evaluation of upper-limb motor function. However, caution is warranted regarding the selection of variables and tasks, particularly for metrics with limited robustness.