CROSSING Project
Title: Real-Time-Human-Performance-Monitoring
Approval Date: March 21
Summary
In a collaborative context with experts from Crossing (AI, Human Factors, Signal Processing, Embedded Systems) the objective is to develop a state model (stress, fatigue, attention, performance) of a human operator with a real-time sensor fusion architecture using physiological, behavioral and environmental data. After an in-depth study of available sensors (on-body or in the environement) and associated techniques from the state-of-the-art, we will establish a robust experimental design and adaptable statistical model to facilitate measurement of the key relevant metrics. The development of the state model may benefit from machine learning to capture and understand individual and hybrid team performance from multi-modal data sources.The ultimate aim is get a model to used later to adapt, according to a team performance criterion, the distribution of tasks (load, complexity) between an operator and autonomous agent (AI).
Related roadmap thrusts and axis
– Thrust 1: New Models to Understand and Anticipate Human Behaviour
- Axis 1.1, 1.2
– Thrust 2: New Algorithms for Energy-Efficient and Human Based AI
- Axis 2.3
Project Members
| Name | Organisation | Role |
| Dr. Nathan Beu | CNRS | Main investigator |
| Prof. Anna Ma-Wyatt | The University of Adelaide | HF, psychology |
| Prof. Cedric Buche | CNRS, ENIB |
AI, Human/Machine |
| As/Prof. Philippe Rauffet | CNRS, UBS |
HF, Human Performance monitoring. |
| Dr. Jean-Philippe Diguet | CNRS | Real-Time, Embedded systems, Sensor fusion |
| Prof. Siobhan Banks | University of South Australia | HF, Fatigue. |
| non exhaustive list …. |
Project Funding
- AID
Start / Duration
- Nov. 2021, 18 Months years