Surg-STAMPS: Synchronized Temporal Analytics of Motion, Physiology, and Speech in Surgery
Abstract
Real-time monitoring of team dynamics has great potential to enhance performance and safety in high-stakes collaborative environments such as surgical operating rooms. This work introduces the design of a novel multimodal data-collection system, Surg-STAMPS (synchronized temporal analytics of motion, physiology, and speech in surgery), that integrates cap-mounted motion trackers, sensor-embedded garments, and mask-mounted microphones to record movement, physiological signals, and vocal communication with minimal disruption. A detailed case study of an open right hepatectomy illustrates the platform's ability to capture physiological and behavioral proxies associated with stress, workload, and communication patterns across different clinical roles and procedure phases. Subjective assessments from 30 participants across 15 real surgical procedures highlight high acceptability and practicality, indicating feasibility for widespread adoption. By providing detailed, real-time data that are often difficult to capture in dynamic environments, such systems can inform targeted interventions and improve team efficiency in surgery and other domains such as trauma centers, search-and-rescue, and construction.
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BibTeX
@article{allemangtrivalle2026surgstamps,
title = {{Surg-STAMPS: Synchronized Temporal Analytics of Motion, Physiology, and Speech in Surgery}},
author = {Arnaud Allemang-Trivalle and Clément Cormi and Nesrine Mekhenane and Eric Vibert and Mathieu Chollet and Katherine Kuchenbecker and Caroline Cao},
journal = {IEEE Journal of Biomedical and Health Informatics},
year = {2026},
month = nov,
doi = {10.1109/jbhi.2026.3723003},
url = {https://utt.hal.science/hal-05853137v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Surg-STAMPS: Synchronized Temporal Analytics of Motion, Physiology, and Speech in Surgery
AU - Arnaud Allemang-Trivalle
AU - Clément Cormi
AU - Nesrine Mekhenane
AU - Eric Vibert
AU - Mathieu Chollet
AU - Katherine Kuchenbecker
AU - Caroline Cao
PY - 2026
DA - 2026/11/05
JO - IEEE Journal of Biomedical and Health Informatics
DO - 10.1109/jbhi.2026.3723003
UR - https://utt.hal.science/hal-05853137v1
AB - Real-time monitoring of team dynamics has great potential to enhance performance and safety in high-stakes collaborative environments such as surgical operating rooms. This work introduces the design of a novel multimodal data-collection system, Surg-STAMPS (synchronized temporal analytics of motion, physiology, and speech in surgery), that integrates cap-mounted motion trackers, sensor-embedded garments, and mask-mounted microphones to record movement, physiological signals, and vocal communication with minimal disruption. A detailed case study of an open right hepatectomy illustrates the platform's ability to capture physiological and behavioral proxies associated with stress, workload, and communication patterns across different clinical roles and procedure phases. Subjective assessments from 30 participants across 15 real surgical procedures highlight high acceptability and practicality, indicating feasibility for widespread adoption. By providing detailed, real-time data that are often difficult to capture in dynamic environments, such systems can inform targeted interventions and improve team efficiency in surgery and other domains such as trauma centers, search-and-rescue, and construction.
SN - 2168-2194
ER -