HuBar: A Visual Analytics Tool to Explore Human Behaviour based on fNIRS in AR guidance systems

TL;DR

HuBar is a visual analytics tool designed to explore human behavior in AR guidance systems by analyzing multi-session biometric and motion data, aiding understanding of non-linear task performance.

Contribution

The paper introduces HuBar, a novel visual analytics system that visualizes multi-session biometric and motion data to analyze user behavior in non-linear AR tasks.

Findings

Effective comparison of user performance across sessions.

Insights into perception, attention, and memory during tasks.

Validation through case studies and expert evaluation.

Abstract

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach by conducting an in-depth examination of a think-aloud experiment with five domain experts.