Evidence map›Paper›PMID 42614668›Full record

ArticleExtended abstracts on Human factors in computing systems. CHI Conference2026

Bounding Boxes in Practice: Design and Early Evaluation of a Video-Based PPE Compliance Monitoring Application for Time-Critical Care: A preliminary evaluation of a video-based explainable interface to improve the usability of AI model outputs.

Christine D Ikponmwonba, Vidhi Shah, Sifan Yuan, Ivan Marsic, Randall Burd, Aleksandra Sarcevic

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In one paragraph

Article in Extended abstracts on Human factors in computing systems. CHI Conference, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Christine D IkponmwonbaCollege of Computing and Informatics, Drexel University.
Vidhi ShahWestphal College of Media Arts and Design, Drexel University.
Sifan YuanDepartment of Electrical and Computer Engineering, Rutgers University.
Ivan MarsicDepartment of Electrical and Computer Engineering, Rutgers University.
Randall BurdDivision of Trauma and Burn Surgery, Children's National Hospital.
Aleksandra SarcevicCollege of Computing and Informatics, Drexel University.

Funding

Development of a Video-based Personal Protective Equipment Monitoring SystemR01EB032819 · NIBIB · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., LINGURARU, MARIUS GEORGE · 2023 to 2025
$1.5M
NIBIB NIH HHS R01 EB032819
6 · The paper itself

Abstract

We describe the design and initial evaluation of a video-based explainable interface (XUI) for personal protective equipment (PPE) compliance monitoring in a dynamic medical setting of trauma resuscitation. The system integrates video input, a computer vision model for detecting PPE status, backend processing, and the video-based XUI. The initial UI underwent a major redesign and was evaluated with five nurse educators. Participants interacted with the XUI features, provided feedback on visual elements, and compared and ranked alternate design options. Using the results, we determined visuals for alert symbols, PPE status, and compliance metrics that support rapid PPE compliance monitoring among care providers. While standard computer vision outputs, such as bounding boxes, improve explainability in object detection models, we suggest moving beyond these model representations to enhance system usability in practice.

Indexed as

AI model outputsEmergency medicineEmpirical studies in interaction designExplainable user interface (XUI)Human-centered computingInteraction designPersonal protective equipment (PPE)Trauma resuscitationVideo-based monitoring systems

Identifiers

PMID42614668
PMCPMC13482928

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.