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...

A HardWare Implementation of the PWM inspired encoding/decoding algorithm (IHA-PWM). A new supervised training approach of SNN for regression based on both backpropagation and PWM inspired encoding/decoding algorithm. Define the specification for a neuromorphic HardWare implementation of both IHA-PWM and the new supervised training algorithm.