Name: FELIPE RAMIREZ CORTES
Publication date: 17/09/2025
Examining board:
Name |
Role |
|---|---|
| CAMILO ARTURO RODRIGUEZ DIAZ | Presidente |
| JEAN CARLOS CARDOZO DA SILVA | Examinador Externo |
| MARCELO EDUARDO VIEIRA SEGATTO | Coorientador |
| MARIANA LYRA SILVEIRA | Examinador Interno |
Summary: 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 thesis 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.
Keywords: Gesture Identification, Forcemyography, Surface Electromyography, Fiber
Bragg Grating, Prosthesis Control, Machine Learning, Virtual Reality.
