Leadership Assessment in Pediatric Intensive Care Unit Team Training
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914010223345664 |
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| author | Ouyang, Liangyang Sakai, Yuki Furuta, Ryosuke Nozawa, Hisataka Matsui, Hikoro Sato, Yoichi |
| author_facet | Ouyang, Liangyang Sakai, Yuki Furuta, Ryosuke Nozawa, Hisataka Matsui, Hikoro Sato, Yoichi |
| contents | This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision. We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment. In order to capture these multimodal signals, we employ Aria Glasses to record egocentric video, audio, gaze, and head movement data. We collect one-hour videos of four simulated sessions involving doctors with different roles and levels. To automate data processing, we propose a method leveraging REMoDNaV, SAM, YOLO, and ChatGPT for fixation object detection, eye contact detection, and conversation classification. In the experiments, significant correlations are observed between leadership skills and behavioral metrics, i.e., the output of our proposed methods, such as fixation time, transition patterns, and direct orders in speech. These results indicate that our proposed data collection and analysis framework can effectively solve skill assessment for training PICU teams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leadership Assessment in Pediatric Intensive Care Unit Team Training Ouyang, Liangyang Sakai, Yuki Furuta, Ryosuke Nozawa, Hisataka Matsui, Hikoro Sato, Yoichi Computer Vision and Pattern Recognition This paper addresses the task of assessing PICU team's leadership skills by developing an automated analysis framework based on egocentric vision. We identify key behavioral cues, including fixation object, eye contact, and conversation patterns, as essential indicators of leadership assessment. In order to capture these multimodal signals, we employ Aria Glasses to record egocentric video, audio, gaze, and head movement data. We collect one-hour videos of four simulated sessions involving doctors with different roles and levels. To automate data processing, we propose a method leveraging REMoDNaV, SAM, YOLO, and ChatGPT for fixation object detection, eye contact detection, and conversation classification. In the experiments, significant correlations are observed between leadership skills and behavioral metrics, i.e., the output of our proposed methods, such as fixation time, transition patterns, and direct orders in speech. These results indicate that our proposed data collection and analysis framework can effectively solve skill assessment for training PICU teams. |
| title | Leadership Assessment in Pediatric Intensive Care Unit Team Training |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.24389 |