SAVOR project

Self-Adaptive and context-aware intelligent training systems in sensorised immersive Virtual Reality environments for Occupational Risk Prevention

Project summary

Occupational Risk Prevention (ORP) aims to train workers in good health and safety habits and is essential to complete the technical training required to use new loading equipment such as forklifts and telehandlers. This is paramount in key sectors such as industry, construction, and agriculture, where a large number of accidents at work are concentrated in the country’s companies, causing a human tragedy, additional costs to the health system and a significant loss of productivity for companies. Previous studies have shown that adaptive learning (where the problem, stimulus or task varies according to the learner’s performance) significantly improves learning outcomes. Immersive Virtual Reality (IVR) enables to simulate complex situations, but so far it only offers ad-hoc solutions for specific scenarios that do not adapt to different learning styles (experience, reaction to risk situations, gender…).

The aim of this project is to design and implement a self-adaptive system for training the operation of these machines in virtual environments, which will represent a significant advance in the ORP of these key sectors.

The main challenge is to make the IVR simulator “intelligent” and to turn it into an adaptive simulator, capable of adapting in real time to the needs and rhythms of the user by regulating the virtual environment and through intelligent agents (avatars). To this end, based on multimodal datasets of users and environments, advanced artificial intelligence (AI) techniques will be studied and applied, allowing the system to be scalable to new learning tasks, including the development of new algorithms if necessary. Among others, the project proposes to exploit the use of semi-supervised learning (to reduce the need for labelled data by experts), unbalanced, real-time learning, feature selection, and transfer learning (key to improving the simulator performance of one domain or machine by using data from another).

 Simplified diagram of an adaptive system, as envisioned in the SAVOR project
Simplified diagram of an adaptive system, as envisioned in the SAVOR project

The final outcome of the project (a product with TRL 6 maturity) will be tested in the facilities of several collaborating companies and public training organisations (8 in total) that have expressed interest in the project and have committed to collaborate in the tasks of data collection with users and validation of results.  

 

Buscamos personas voluntarias para Experimento de Realidad Virtual con sensores fisiológicos

Estamos llevando a cabo un experimento, y necesitamos vuestra colaboración.

🔬 Objetivo:

Conocer y analizar distintas respuestas fisiológicas en un entorno experimental de realidad virtual mediante sensores fisiológicos. Como voluntario/a, te sumergirás en un entorno virtual mientras se registran tus señales fisiológicas como:

  • Frecuencia cardíaca 🫀
  • Conductancia de la piel ⚡

⏳ ¿Cuánto dura la prueba?

Aproximadamente 20 minutos, incluyendo la firma del consentimiento, la medición de valores iniciales (baseline) y la experiencia en Realidad Virtual.

📍 Ubicación:

Escuela Politécnica Superior

📅 Fechas disponibles:

Desde el martes 4 de febrero hasta el jueves 13 de febrero de 2025.

🕘 Horarios disponibles:

Mañanas: 9:00 – 14:00
Tardes: 16:00 – 19:00

⚠️ Importante: La participación es voluntaria y se requerirá la firma de un consentimiento informado antes del inicio del experimento.

¡Gracias por tu colaboración! 😊

📝 Apúntate en este formulario

Experimento de Realidad Virtual

PID2023-150694OA-I00 funded by MICIU/AEI/ 10.13039/501100011033 and by “ERDF/EU”