Biomechanical Hand Model: Modeling and Simulating the Lateral Pinch Movement - Lemos, A.F.; Rodrigues da Silva, L. A.; Nagy, B. V.; Barroso, P. N.; Vimieiro, C. B. S. (Experimental mechanics, 2024)
Background Hand movements are crucial in daily activities, sparking extensive interest and research in biomechanical models. While existing models offer valuable insights, their complexity and processing costs may limit their suitability for all applications, sometimes impeding research efficiency. Objectives This study aimed to develop a biomechanical model of the human hand for analyzing the physiology of lateral pinch movement. Unlike conventional methodologies, this approach focuses on delivering a computationally efficient model while incorporating the trapeziometacarpal joint into the analysis. Methods The model, which operates in a multibody environment, simulates lateral pinching movement by applying external time-varying torques to digit joints, emulating musculature, tendons, and ligaments. Torque estimation was achieved through the Euler-Lagrange approach. The model generates animated representations of the movement, aiding pathology identification and outputting dynamic variables. The model’s was validated through data acquired from asymptomatic subjects via an OptiTrack system. Results The average disparity between the expected and obtained joint angular displacements was 6.06 % and 1.90 % during validation and verification stages, suggesting high fidelity in the model performance. Correlation analysis revealed strong positive linear relationships and robust correlations between the obtained and expected configuration data. Model-generated pinch postures closely resembled expected physiological patterns, with results falling within the range for asymptomatic individuals documented in the scientific literature. Conclusion The system efficiently analyzes dynamic variables at a low computational cost, offering animated representations for pathology identification. The model’s potential for rehabilitation solutions and adaptability, coupled with its accuracy and versatility, make it an asset for advancing hand biomechanics research.