A New Approach for Quantification of Finger Angles with Applications in Rehabilitation and Medical Assessment
The biomechanical evaluation of the finger joint angle (FJA) is a fundamental aspect in medical diagnosis and neuromuscular rehabilitation, with direct implications for planning therapeutic strategies and optimizing functional recovery. Currently, FJA quantification methods range from conventional t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Romanian Association of Balneology, Editura Balneara
2025-01-01
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| Series: | Balneo and PRM Research Journal |
| Subjects: | |
| Online Access: | https://bioclima.ro/Balneo766.pdf |
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| Summary: | The biomechanical evaluation of the finger joint angle (FJA) is a fundamental aspect in medical diagnosis and neuromuscular rehabilitation, with direct implications for planning therapeutic strategies and optimizing functional recovery. Currently, FJA quantification methods range from conventional techniques, such as goniometer measurements, to advanced approaches based on Bragg grating fibre-optic strain sensors (FBG) and inertial measurement units (IMU). This study proposes an innovative computational geometric methodology for estimating the flexion and extension angles of finger joints, utilizing IMU sensors integrated into a hardware system based on the ESP32 microcontroller, capable of transmitting real-time data to a dedicated system. A MATLAB graphical user interface (GUI) is used for visualizing and interpreting relevant kin-ematic parameters. Experimental results analysis revealed a maximum approximation error of approximately 3% after implementing a rigorous calibration procedure, using a classical reference method. These findings demonstrate the feasibility of integrating the proposed method into a broader clinical framework for objective monitoring of patient progress in functional rehabilitation programs. The study opens new perspectives for the development of advanced data processing algorithms, including the integration of deep learning neural networks for modelling and optimizing joint movements |
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| ISSN: | 2734-8458 |