Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit the ecological validity of the movement. In this study, we propose a methodological adaptation of the protocol in a standing position to allow a more physiological elbow configuration and to increase the dynamic range of elbow and shoulder motion. The objective was to characterize kinematic patterns of the hand-to-mouth movement in typically developing children aged 4 to 9 years using this adapted setup. This study was designed as a descriptive analysis and does not aim to provide formal validation of the standing protocol against the original seated configuration. An observational study that included 40 children was conducted. Motion data were acquired using eight optoelectronic cameras (sampling frequency: 250 Hz) and 17 reflective markers placed on the trunk and upper limbs. Kinematic patterns and spatiotemporal parameters were computed using dedicated motion analysis software. No significant differences were observed between dominant and non-dominant limbs in spatiotemporal parameters, whereas kinematic differences were minimal and limited to trunk rotation, as identified by Statistical Parametric Mapping (SPM). Some isolated statistically significant associations with age were identified in specific spatiotemporal variables; however, these variables showed low coefficients of determination (R2), indicating limited explanatory power of age. Overall, kinematic parameters did not exhibit consistent age-related patterns. These findings provide preliminary descriptive data for hand-to-mouth kinematics in a standing condition, which may contribute to the future development of assessment protocols. However, the limited sample size and the absence of pathological populations restrict the direct generalization of these findings. Future studies should evaluate the applicability of this approach in clinical cohorts and explore its integration into sensor-based and data-driven models for movement analysis.
Moreno et al. (Thu,) studied this question.