Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.

Journal: International journal of computer assisted radiology and surgery

PMID: 38955902

Abstract

PURPOSE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.

Authors

Daniel Caballero

Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Center, Cáceres, Spain.
Manuel J Pérez-Salazar

Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Center, Cáceres, Spain.
Juan A Sánchez-Margallo

Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Center, Cáceres, Spain. jasanchez@ccmijesususon.com.
Francisco M Sánchez-Margallo

Jesús Usón Minimally Invasive Surgery Centre, Ctra. N-521, km 41.8, 10071 Cáceres, Spain. Electronic address: msanchez@ccmijesususon.com.

Keywords

Adult Artificial Intelligence Ergonomics Female Galvanic Skin Response Humans Male Minimally Invasive Surgical Procedures Occupational Stress Robotic Surgical Procedures Surgeons

External Resources

View on PubMed Access via DOI PubMed (38955902)

Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.

Abstract

Authors

Keywords

External Resources

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