AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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2024

Stuart, J.; Stephen, A.; Aul, K.; Bumbach, M. D.; Huffman, S.; Russo, B.; Lok, B.

Developing augmented reality filters to display visual cues on diverse skin tones Journal Article

In: Frontiers in Virtual Reality, vol. 5, 2024, ISSN: 26734192 (ISSN).

Abstract | Links | BibTeX | Tags: Augmented Reality, fidelity, Healthcare, realism, simulation, symptoms, visual cue training

@article{stuart_developing_2024,

title = {Developing augmented reality filters to display visual cues on diverse skin tones},

author = {J. Stuart and A. Stephen and K. Aul and M. D. Bumbach and S. Huffman and B. Russo and B. Lok},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198640398&doi=10.3389%2ffrvir.2024.1363193&partnerID=40&md5=33470de917a5e9979f77fd42f25614eb},

doi = {10.3389/frvir.2024.1363193},

issn = {26734192 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Frontiers in Virtual Reality},

volume = {5},

abstract = {Introduction: Variations in skin tone can significantly alter the appearance of symptoms such as rashes or bruises. Unfortunately, previous works utilizing Augmented Reality (AR) in simulating visual symptoms have often failed to consider this critical aspect, potentially leading to inadequate training and education. This study seeks to address this gap by integrating generative artificial intelligence (AI) into the AR filter design process. Methods: We conducted a 2 × 5 within-subjects study with second-year nursing students (N = 117) from the University of Florida. The study manipulated two factors: symptom generation style and skin tone. Symptom generation style was manipulated using a filter based on a real symptom image or a filter based on a computer-generated symptom image. Skin tone variations were created by applying AR filters to computer-generated images of faces with five skin tones ranging from light to dark. To control for factors like lighting or 3D tracking, 101 pre-generated images were created for each condition, representing a range of filter transparency levels (0–100). Participants used visual analog scales on a computer screen to adjust the symptom transparency in the images until they observed image changes and distinct symptom patterns. Participants also rated the realism of each condition and provided feedback on how the symptom style and skin tone impacted their perceptions. Results: Students rated the symptoms displayed by the computer-generated AR filters as marginally more realistic than those displayed by the real image AR filters. However, students identified symptoms earlier with the real-image filters. Additionally, SET-M and Theory of Planned Behavior questions indicate that the activity increased students’ feelings of confidence and self-efficacy. Finally, we found that similar to the real world, where symptoms on dark skin tones are identified at later stages of development, students identified symptoms at later stages as skin tone darkened regardless of cue type. Conclusion: This work implemented a novel approach to develop AR filters that display time-based visual cues on diverse skin tones. Additionally, this work provides evidence-based recommendations on how and when generative AI-based AR filters can be effectively used in healthcare education. Copyright © 2024 Stuart, Stephen, Aul, Bumbach, Huffman, Russo and Lok.},

keywords = {Augmented Reality, fidelity, Healthcare, realism, simulation, symptoms, visual cue training},

pubstate = {published},

tppubtype = {article}

}

Introduction: Variations in skin tone can significantly alter the appearance of symptoms such as rashes or bruises. Unfortunately, previous works utilizing Augmented Reality (AR) in simulating visual symptoms have often failed to consider this critical aspect, potentially leading to inadequate training and education. This study seeks to address this gap by integrating generative artificial intelligence (AI) into the AR filter design process. Methods: We conducted a 2 × 5 within-subjects study with second-year nursing students (N = 117) from the University of Florida. The study manipulated two factors: symptom generation style and skin tone. Symptom generation style was manipulated using a filter based on a real symptom image or a filter based on a computer-generated symptom image. Skin tone variations were created by applying AR filters to computer-generated images of faces with five skin tones ranging from light to dark. To control for factors like lighting or 3D tracking, 101 pre-generated images were created for each condition, representing a range of filter transparency levels (0–100). Participants used visual analog scales on a computer screen to adjust the symptom transparency in the images until they observed image changes and distinct symptom patterns. Participants also rated the realism of each condition and provided feedback on how the symptom style and skin tone impacted their perceptions. Results: Students rated the symptoms displayed by the computer-generated AR filters as marginally more realistic than those displayed by the real image AR filters. However, students identified symptoms earlier with the real-image filters. Additionally, SET-M and Theory of Planned Behavior questions indicate that the activity increased students’ feelings of confidence and self-efficacy. Finally, we found that similar to the real world, where symptoms on dark skin tones are identified at later stages of development, students identified symptoms at later stages as skin tone darkened regardless of cue type. Conclusion: This work implemented a novel approach to develop AR filters that display time-based visual cues on diverse skin tones. Additionally, this work provides evidence-based recommendations on how and when generative AI-based AR filters can be effectively used in healthcare education. Copyright © 2024 Stuart, Stephen, Aul, Bumbach, Huffman, Russo and Lok.