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Development of artificial intelligence and multi-sensor-based dexterity assessment system: performance evaluation
Mehmet Emin Aktan1,2, Sena Zeybek Kılıç3, Erhan Akdoğan4
1Department of Mechatronics Engineering, Bartın University, 74110, Bartın, Türkiye. maktan@bartin.edu.tr.
Medical & Biological Engineering & Computing
|June 16, 2025
Summary
This study presents an automated system for precise manual dexterity assessment using AI and multimodal sensors. It overcomes traditional limitations, offering accurate and objective evaluation of fine motor skills for clinical and professional use.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Artificial Intelligence in Healthcare
Background:
- Manual dexterity is crucial for disease diagnosis and skill evaluation.
- Traditional assessments are subjective, time-consuming, and prone to errors due to expert reliance.
Purpose of the Study:
- To develop and evaluate an automated system for high-precision manual dexterity assessment.
- To integrate multiple sensors and AI for objective fine motor skill evaluation.
Main Methods:
- Utilized electromyography (EMG), inertial measurement units (IMU), and image processing.
- Developed AI algorithms to classify movements, analyze muscle activity, and measure durations.
- Implemented an expert system for multimodal sensor data interpretation.
Main Results:
- The automated system accurately assessed hand dexterity in a study with 20 participants.
- Demonstrated capability for automatic and precise evaluation of fine motor control.
Conclusions:
- The developed system offers an accurate and objective alternative to traditional manual dexterity tests.
- AI-powered multimodal sensing provides a robust solution for clinical and professional skill assessment.
