The main objective of this project is the design and development of an intelligent decision support system based on machine learning to identify the muscles affected by spasticity as well as objectively quantify the level of deterioration in both the upper and lower limbs. The approach is focused on stroke patients and aims to minimize the invasiveness of the assessment by using high-definition images of the limbs of the patient as input of a HPD system.

This project presents an intelligent crutch that enables continuous and personalized diagnosis of patients with functional mobility impairment. The proposed solution is based on Neural Sensors and Personalized Diagnostic Virtual Sensors, all of them designed using different Artificial Intelligence techniques, such as Spiking Neural Networks and Anomaly Detection techniques. As a result, a personalized diagnosis will make it easier for therapists to establish better and more precise personalized therapies, with the...

The main challenge of this approach is to characterize the state of the patient in a given time, so that the Virtual Sensor is able to detect slight variations in this state. The final goal is to provide therapists with information that allows them to adapt easily the rehabilitation therapies, so that their efficiency is maximized, which will impact on the quality of life of patients.