Gesture recognition for prosthesis control using electromyography and force myography based on optical fiber sensors
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Data
2025-09-17
Autores
Ramirez Cortés, Felipe
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Universidade Federal do Espírito Santo
Resumo
Amputation is the partial or total loss of a limb. It is a challenging event that affects people worldwide, with an estimated prevalence of 552.45 million in 2019 and a growing rate. The loss of an upper limb, in particular, strongly affects a person’s ability to perform activities of daily living (ADL), communicate, and interact with their environment. To restore lost functionality, assistive devices known as prostheses have been developed. Modern active prostheses can be controlled by interpreting the user’s movement intention through various biological signals, such as Surface Electromyography (sEMG), which measures the electrical activity of muscles. While sEMG is an established and predominant control method, it has limitations. Forcemyography (FMG) is a technique that measures changes in muscle volume and pressure during contraction. It has emerged as a promising alternative, offering advantages such as greater signal stability and reduced sensitivity to skin conditions like sweat. This master’s dissertation proposes and evaluates a hybrid sensor system combining FMG and sEMG to create a more robust and precise method for hand gesture classification. The system integrates a custom-developed FMG sensor, which uses a Fiber Bragg Grating (FBG) embedded within a flexible 3D-printed structure, with a commercial sEMG sensor. The primary goal is to improve the control of real and virtual prosthetic hands for amputees. The study involved recording signals from able-bodied subjects while they performed tasks involving different hand angles and grip forces. Data from the sEMG, FMG, and the combined hybrid system were used to train and test seven different machine learning algorithms, with the dataset split into 80% for training and 20% for testing. Results showed that the optimal sensing strategy is task-dependent. For angle classification, the hybrid FMG-sEMG sensor achieved the highest accuracy of 85.62% with the K-Nearest Neighbors (KNN) classifier. For force classification, the sEMG sensor alone was superior, reaching an accuracy of 92.53% with a Support Vector Machine (SVM). Furthermore, the hybrid system’s feasibility for real-time application was validated in a Virtual Reality (VR) environment, where it achieved 99.83% accuracy in classifying binary open/close hand gestures. This research demonstrates the complementary nature of FMG and sEMG signals, concluding that a multimodal approach can be used to develop more sophisticated, reliable, and intuitive control systems for upper-limb prostheses by selecting the best sensing modality for the desired task
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Identificação de gestos , Força-miografia , Eletromiografia de superfície , Grade de Bragg de fibra , Controle de próteses , Aprendizado de máquina , Realidade virtual , Gesture Identification , Forcemyography , Surface Electromyography , Fiber Bragg Grating , Prosthesis Control , Machine Learning , Virtual Reality