EHU ViSens | Research Areas | Application of Artificial Intelligence for the Development of a Personalized Intelligent Crutch (NEUROTIP)

The Project

Project Publications

Methodology based on spiking neural networks for univariate time-series forecasting

Publication Data

Lucas, S., Portillo, E. Methodology based on spiking neural networks for univariate time-series forecasting, Neural Networks, vol. 173, No. 106171, pp. 1-22, 2024. JCR Impact Factor: 7.8 (2024). Quartile: Q1. https://doi.org/10.1016/j.neunet.2024.106171
Machine learning-based gait anomaly detection using a sensorized tip: an individualized approach

Publication Data

Otamendi, J., Zubizarreta, A., Portillo, E. Machine learning-based gait anomaly detection using a sensorized tip: an individualized approach, Neural Computing and Applications, pp. 1-17, 2023. JCR Impact Factor: 6.0 (2022). Quartile: Q2. https://doi.org/10.1007/s00521-023-08601-1
Aplicación de técnicas de aprendizaje automático para la clasificación de actividades mediante una muleta inteligente para esclerosis múltiple

Publication Data

Lucas, S., Brull, A., Portillo, E., Zubizarreta, A., Cabanes, I. Aplicación de técnicas de aprendizaje automático para la clasificación de actividades mediante una muleta inteligente para esclerosis múltiple, XLII Jornadas de Automática, Castellón, 2021. ISBN: 978-84-9749-804-3. https://doi.org/10.17979/spudc.9788497498043.083

see all

Application of Artificial Intelligence for the Development of a Personalized Intelligent Crutch (NEUROTIP)

Lead Researchers: Eva Portillo Pérez , Ana Rodriguez Larrad - (01/09/2022 - 31/12/2023)

Research Area: Applications in bioengineering

Funding Body: Euskampus Fundazioa. Euskampus Missions 2.0 programme

Objectives

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 consequent improvement in the patient's prognosis and quality of life.

Achievements

Amongst the achievements of the project, we can highlight the following:

Design of Neural sensors for estimating lateromedial and anteroposterior angles based on both ANN and SNN
A personalized approach based on Machine Learning for Gait Anomaly Detection using a Sensorized Tip

back