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This study investigates the biomechanical and kinematic features of reach-to-grasp motions in pediatric populations, focusing on joint angles and trajectory patterns to explore motor control and flexibility. The dataset includes 25 typically developing children (ages 5–18) and two children with moderate left-sided spastic cerebral palsy (CP). Boxplots are used for data visualization, highlighting the variability and compensatory patterns in trunk, shoulder, elbow, and wrist movements. CP participants exhibit greater variability, with the older CP individual (Male 10.17y) showing improved control compared to the younger one (Male 7.08y). Correlation matrices compare biomechanical dependencies, revealing strong coordinated upper-limb and trunk dynamics in healthy participants and disrupted interactions or compensatory mechanisms in CP individuals. Principal Component Analysis (PCA) is employed to compare movement characteristics across age groups and health conditions, with results showing that two principal components explain 99% of age-related variance, while four components capture 95% of task-specific movement variability. These findings establish clinically relevant benchmarks for identifying deviations in movement patterns and designing targeted rehabilitation programs to enhance functional motor control in children with movement disorders.

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Kinematic Features in Reach-and-Grasp Cycle for Healthy and CP-Affected Pediatric Populations: An Exploratory Data Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Madhav Pandey,
  • Subhash Pratap,
  • Jyotindra Narayan,
  • Shiv Manjaree Gopaliya

摘要

This study investigates the biomechanical and kinematic features of reach-to-grasp motions in pediatric populations, focusing on joint angles and trajectory patterns to explore motor control and flexibility. The dataset includes 25 typically developing children (ages 5–18) and two children with moderate left-sided spastic cerebral palsy (CP). Boxplots are used for data visualization, highlighting the variability and compensatory patterns in trunk, shoulder, elbow, and wrist movements. CP participants exhibit greater variability, with the older CP individual (Male 10.17y) showing improved control compared to the younger one (Male 7.08y). Correlation matrices compare biomechanical dependencies, revealing strong coordinated upper-limb and trunk dynamics in healthy participants and disrupted interactions or compensatory mechanisms in CP individuals. Principal Component Analysis (PCA) is employed to compare movement characteristics across age groups and health conditions, with results showing that two principal components explain 99% of age-related variance, while four components capture 95% of task-specific movement variability. These findings establish clinically relevant benchmarks for identifying deviations in movement patterns and designing targeted rehabilitation programs to enhance functional motor control in children with movement disorders.