One of the challenges of human-swarm interaction (HSI) is how to manage the operator's workload. In order to do this, we propose a novel neurofeedback technique for the real-time measurement of workload using functional near-infrared spectroscopy (fNIRS). The objective is to develop a baseline for workload measurement in human-swarm interaction using fNIRS and to develop an interface that dynamically adapts to the operator's workload. The proposed method consists of using fNIRS device to measure brain activity, process this through a machine learning algorithm, and pass it on to the HSI interface. By dynamically adapting the HSI interface, the swarm operator's workload could be reduced and the performance improved.
@article{arxiv.2405.07834,
title = {Adaptive Human-Swarm Interaction based on Workload Measurement using Functional Near-Infrared Spectroscopy},
author = {Ayodeji O. Abioye and Aleksandra Landowska and William Hunt and Horia Maior and Sarvapali D. Ramchurn and Mohammad Naiseh and Alec Banks and Mohammad D. Soorati},
journal= {arXiv preprint arXiv:2405.07834},
year = {2024}
}
Comments
This paper consist of 3 pages and contains 2 figures. This abstract paper was presented at the "Breaking Swarm Stereotypes" workshop of the 2024 IEEE International Conference on Robotics and Automation (ICRA) in PACIFICO Yokohama, Japan, held from May 13th to 17th, 2024. https://hauertlab.com/breaking-swarm-stereotypes-workshop-icra-2024/