Multimodal Physiological Assessment of the Task-related Attentional States in a VR Driving Environment

Zhu, Wenchao; Kucyi, Aaron; Kramer, Arthur F.; Lin, Yingzi

doi:10.1109/m2vip55626.2022.10041103

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Physiological signals have been established as reliable indicators of an individual’s varying states (Zhu, Kucyi, Kramer, & Lin, 2022). Key signals, including ECG, respiration, and temperature, were first preprocessed using min-max standardization (Van Gent, Farah, Nes, & van Arem, 2018).…”

Section: Resultsmentioning

confidence: 99%

COMPASS App: A Patient-centered Physiological based Pain Assessment System

Zhu,

Liu,

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications.

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Section: Resultsmentioning

confidence: 99%