Adaptive Human-Swarm Interaction based on Workload Measurement using Functional Near-Infrared Spectroscopy

TL;DR

This paper introduces a neurofeedback-based method using fNIRS to measure operator workload in real-time during human-swarm interaction, enabling dynamic interface adaptation to improve performance.

Contribution

It presents a novel real-time workload measurement technique using fNIRS combined with machine learning for adaptive human-swarm interfaces.

Findings

fNIRS effectively measures workload in real-time

Adaptive interface reduces operator workload

Performance improvements observed with dynamic adaptation

Abstract

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.