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<p>Manual dexterity tests are essential for diagnosing diseases and evaluating professional skills that require fine motor control. Traditional assessments often depend on expert supervision, leading to delays, subjectivity, and inaccuracies. This study introduces an automated dexterity assessment system that integrates multiple sensors and artificial intelligence algorithms for high-precision evaluation. The system classifies hand movements, analyzes muscle contraction levels, and determines hold-release durations using electromyography (EMG), inertial measurement units (IMU), and image processing techniques. An expert system interprets the multimodal sensor data and presents the results to clinicians. A performance evaluation with 20 participants demonstrated the system’s capability to assess hand dexterity automatically and accurately.&#xa0;</p> Graphical Abstract <p></p>

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Development of artificial intelligence and multi-sensor-based dexterity assessment system: performance evaluation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Mehmet Emin Aktan,
  • Sena Zeybek Kılıç,
  • Erhan Akdoğan,
  • Tuğçe Özekli Mısırlıoğlu,
  • Deniz Palamar

摘要

Manual dexterity tests are essential for diagnosing diseases and evaluating professional skills that require fine motor control. Traditional assessments often depend on expert supervision, leading to delays, subjectivity, and inaccuracies. This study introduces an automated dexterity assessment system that integrates multiple sensors and artificial intelligence algorithms for high-precision evaluation. The system classifies hand movements, analyzes muscle contraction levels, and determines hold-release durations using electromyography (EMG), inertial measurement units (IMU), and image processing techniques. An expert system interprets the multimodal sensor data and presents the results to clinicians. A performance evaluation with 20 participants demonstrated the system’s capability to assess hand dexterity automatically and accurately. 

Graphical Abstract