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

## TL;DR

This paper presents an automated system using sensors and AI to accurately assess hand dexterity, reducing reliance on expert supervision.

## Contribution

A novel system combining multi-sensor data and AI for precise, automated dexterity evaluation is developed and tested.

## Key findings

- The system successfully classified hand movements and analyzed muscle activity using EMG and IMU sensors.
- Performance evaluation with 20 participants showed accurate and automatic dexterity assessment.
- The system provides objective results, reducing subjectivity and delays in traditional assessments.

## Abstract

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.