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Handwriting and drawing are complex motor activities that require precise neuromuscular coordination, which can be quantitatively assessed through the integration of physiological and kinematic data. This study presents the development of a portable system designed to simultaneously acquire and analyze electromyographic (EMG) signals from key forearm muscles, along with wrist and elbow joint angle during geometric figure tracing tasks. The system integrates wearable EMG sensors, wireless data transmission and real-time kinematic tracking via video capture using MediaPipe. Fourteen right-handed participants completed five guided geometric tracing tasks using an interactive touchscreen interface. EMG signal features such as RMS were extracted, along with joint angle ranges and tracing accuracy metrics. The results revealed high intersubject variability: RMS values ranged from 45.8 µV to 263.7 µV, with a maximum standard deviation of 113.2 µV, while wrist joint ranges varied from 64.8° to 162.6°. The developed interface allows for individual participant registration and automatic generation of personalized reports, with options for data storage and electronic delivery. By enabling synchronized multimodal data acquisition, automated analysis, and centralized management, this platform offers a comprehensive tool for studying fine motor control, with applications in clinical assessment, rehabilitation, and neuromotor research.

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Multimodal System for Simultaneous Kinematic and EMG Data acquisition in Handwriting

  • 会议论文

  • 发表日期：

    2026年

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  • Maleny López-Juárez,
  • Eduardo Macias-Naranjo,
  • Edgar Huerta-Ledezma,
  • César Guzmán-Zavala,
  • Hugo Vélez-Pérez,
  • Ricardo A. Salido-Ruiz,
  • Rebeca Romo-Vázquez

摘要

Handwriting and drawing are complex motor activities that require precise neuromuscular coordination, which can be quantitatively assessed through the integration of physiological and kinematic data. This study presents the development of a portable system designed to simultaneously acquire and analyze electromyographic (EMG) signals from key forearm muscles, along with wrist and elbow joint angle during geometric figure tracing tasks. The system integrates wearable EMG sensors, wireless data transmission and real-time kinematic tracking via video capture using MediaPipe. Fourteen right-handed participants completed five guided geometric tracing tasks using an interactive touchscreen interface. EMG signal features such as RMS were extracted, along with joint angle ranges and tracing accuracy metrics. The results revealed high intersubject variability: RMS values ranged from 45.8 µV to 263.7 µV, with a maximum standard deviation of 113.2 µV, while wrist joint ranges varied from 64.8° to 162.6°. The developed interface allows for individual participant registration and automatic generation of personalized reports, with options for data storage and electronic delivery. By enabling synchronized multimodal data acquisition, automated analysis, and centralized management, this platform offers a comprehensive tool for studying fine motor control, with applications in clinical assessment, rehabilitation, and neuromotor research.