Exoskeleton for paraplegics based on EMG muscle signals, with artificial neural network and fuzzy logic

Abstract

This project proposes an innovative exoskeleton designed to assist paraplegic individuals, incorporating advanced technologies to optimize its effectiveness. The major innovation lies in the intelligent control system based on predicting the biomechanical movements desired by the user. This system uses machine learning and artificial neural networks to analyze and predict joint movements based on intentions detected in real time. Using a rich database, it synchronizes the device's movements with the user's biomechanical needs. The system adapts dynamically thanks to a fuzzy logic module, which adjusts the torque of the joints according to the forces required to perform each movement. This adaptation takes into account several parameters such as the user's age, weight, and muscle activity, thus guaranteeing personalized and optimal assistance. The exoskeleton also features EMG sensors and measurement systems such as accelerometers and gyroscopes, ensuring precise movement control. All of these technologies allow for the reproduction of natural human body movements while providing fluid and autonomous mobility, thus improving users' quality of life. The project presents a comprehensive, adaptable, and highly personalized solution to overcome the limitations of existing devices.