uxSense: Supporting User Experience Analysis with Visualization and Computer Vision

TL;DR

uxSense is a visual analytics system that leverages machine learning to extract and visualize user behavior from audio and video recordings, aiding usability analysis.

Contribution

It introduces a novel system integrating computer vision and NLP for detailed user behavior extraction and visualization in usability studies.

Findings

Enhanced analysis efficiency for UX researchers

Successful extraction of user sentiment, actions, and speech

Effective visualization of multi-modal data streams

Abstract

Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose uxSense, a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.