[en] Staying in the intensive care unit (ICU) is often traumatic, leading to post-intensive care syndrome (PICS), which encompasses physical, psychological, and cognitive impairments. Currently, there are limited interventions available for PICS. Studies indicate that exposure to visual art may help address the psychological aspects of PICS and be more effective if it is personalized. We develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys) to enable personalized therapeutic visual art experiences for post-ICU patients. We investigate four state-of-the-art VA RecSys engines, evaluating the relevance of their recommendations for therapeutic purposes compared to expert-curated recommendations. We conduct an expert pilot test and a large-scale user study (n=150) to assess the appropriateness and effectiveness of these recommendations. Our results suggest all recommendations enhance temporal affective states. Visual and multimodal VA RecSys engines compare favourably with expert-curated recommendations, indicating their potential to support the delivery of personalized art therapy for PICS prevention and treatment.
Disciplines :
Computer science
Author, co-author :
YILMA, Bereket Abera ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Kim, Chan Mi; University of Twente
Cupchik, Gerald C.; U of T - University of Toronto
LEIVA, Luis A. ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Artful Path to Healing: Using Machine Learning for Visual Art Recommendation to Prevent and Reduce Post-Intensive Care Syndrome (PICS)
Publication date :
11 May 2024
Event name :
Proceedings of the CHI Conference on Human Factors in Computing Systems
Event place :
Hybrid, Honolulu, Usa
Event date :
11-05-2024 => 16-05-2024
By request :
Yes
Audience :
International
Main work title :
CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
HE - 101071147 - SYMBIOTIK - Context-aware adaptive visualizations for critical decision making
FnR Project :
FNR15722813 - BANANA - Brainsourcing For Affective Attention Estimation, 2021 (01/02/2022-31/01/2025) - Luis Leiva
Funders :
CE - Commission Européenne European Union
Funding text :
We thank Thomas Falck, MSc (Philips) and Dr. Esther van der Heide (Philips) for their advice on PICS research, and Prof. Dr. Geke Ludden (University of Twente) and Dr. Thomas van Rompay (University of Twente) for their advice on developing guided art therapy used in this study. We extend our thanks to the participants of our study for sharing their valuable experiences, and to the anonymous reviewers for their constructive comments. This work was supported by the Horizon 2020 FET program of the European Union through the ERA-NET Cofund funding grant CHIST-ERA-20-BCI-001 and the European Innovation Council Pathfinder program (SYMBIOTIK project), and the Top Technology Twente Connecting Industry program (TKI Topsector HTSM), which is partially funded by Philips.
Commentary :
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI 24)
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