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Article

FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation

by
Filippo Laganà
1,*,
Diego Pellicanò
2,
Mariangela Arruzzo
2,
Danilo Pratticò
3,*,
Salvatore A. Pullano
1 and
Antonino S. Fiorillo
1
1
Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science, “Magna Græcia” University, 88100 Catanzaro, Italy
2
ITIS Conte M. M. Milano School, 89024 Polistena, Italy
3
DICEAM Department, “Mediterranea” University, 89122 Reggio Calabria, Italy
*
Authors to whom correspondence should be addressed.
Electronics 2025, 14(11), 2268; https://doi.org/10.3390/electronics14112268 (registering DOI)
Submission received: 8 May 2025 / Revised: 26 May 2025 / Accepted: 30 May 2025 / Published: 31 May 2025
(This article belongs to the Special Issue Circuit Design for Embedded Systems)

Abstract

The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mechanical model, developed in COMSOL 6.2 Multiphysics, simulates the interaction between a robotic glove and a deformable latex sphere. The latex material is described using a two-parameter Mooney–Rivlin hyperelastic formulation to capture large nonlinear deformations under realistic contact conditions. The high-fidelity simulation data are used to validate the signal acquisition chain and to train a supervised AI algorithm capable of classifying rehabilitation progress—whether improving or worsening—based on biomechanical features. An integrated electronic prototype enables seamless data flow to a cloud-based monitoring platform, supporting real-time feedback and adaptability. The classification algorithm demonstrates robust performance across different test conditions, while the electronic system confirms its applicability in rehabilitation settings. The novelty of this paper lies in the closed-loop integration of FEM-based simulation, AI-driven analysis, and embedded electronics into a unified monitoring architecture. This intelligent and non-invasive approach provides a scalable tool for tracking motor recovery and enhancing therapy effectiveness through adaptive, feedback-driven interventions.
Keywords: embedded systems; FEM-modelling; AI monitoring; wearable electronics; signal processing embedded systems; FEM-modelling; AI monitoring; wearable electronics; signal processing

Share and Cite

MDPI and ACS Style

Laganà, F.; Pellicanò, D.; Arruzzo, M.; Pratticò, D.; Pullano, S.A.; Fiorillo, A.S. FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation. Electronics 2025, 14, 2268. https://doi.org/10.3390/electronics14112268

AMA Style

Laganà F, Pellicanò D, Arruzzo M, Pratticò D, Pullano SA, Fiorillo AS. FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation. Electronics. 2025; 14(11):2268. https://doi.org/10.3390/electronics14112268

Chicago/Turabian Style

Laganà, Filippo, Diego Pellicanò, Mariangela Arruzzo, Danilo Pratticò, Salvatore A. Pullano, and Antonino S. Fiorillo. 2025. "FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation" Electronics 14, no. 11: 2268. https://doi.org/10.3390/electronics14112268

APA Style

Laganà, F., Pellicanò, D., Arruzzo, M., Pratticò, D., Pullano, S. A., & Fiorillo, A. S. (2025). FEM-Based Modelling and AI-Enhanced Monitoring System for Upper Limb Rehabilitation. Electronics, 14(11), 2268. https://doi.org/10.3390/electronics14112268

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