IMPACT: A Dataset for Integrated Measurement of Performance and Contextual Task-Related Effects

Abstract

The paper introduces the Integrated Measurement of Performance and Contextual Task-related effects (IMPACT) dataset, designed to facilitate comprehensive investigations into contextual dynamics within human–machine collaboration (HMC) systems. Unlike traditional approaches focusing solely on monitoring physical contextual aspects, IMPACT uniquely incorporates the individual perception of human participants within these systems. Data sources include temporal records of task performance, user models derived from real-time video processing as well as contextual factors such as different light and noise conditions. Key features include task stimuli, user responses, reaction times, emotional states, and attention levels, all synchronized via timestamps and recorded in .csv and .mp4 formats. Our analysis highlights variations in user perceptions and performance under different contextual states, both in person-independent and person-specific scenarios. Pre-test and post-test questionnaire data reveal shifts in user perceptions of light and noise as distractors. Performance data indicate that task-related adaptations maintain consistent performance levels despite contextual changes, while attention and arousal levels vary significantly. Person-specific analysis underscores the importance of individualized context adaptation, as users exhibit unique responses to environmental changes. The IMPACT dataset supports the development of adaptive human–machine collaboration systems by integrating individual user perceptions with objective context monitoring. Future research will focus on refining context-adaptive models to enhance the robustness and accuracy of individualized context-related performance predictions.

Keywords

• HMI
• HMC
• collaborative systems
• context-related adaptation
• contextual perception