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Correlation Study and Predictive Modelling of Ergonomic Parameters in Robotic-Assisted Laparoscopic Surgery
Manuel J Pérez-Salazar1, Daniel Caballero1, Juan A Sánchez-Margallo1
1Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, ES-10071 Cáceres, Spain.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
Robotic-assisted surgery (RAS) shows a link between poor posture and muscle strain. Surgeon experience impacts ergonomics, with novices exhibiting better posture than experts, informing risk prevention.
Area of Science:
- Ergonomics
- Robotic-assisted surgery
- Surgical technology
Background:
- Investigating objective analysis of ergonomic conditions in robotic-assisted surgery (RAS).
- Seeking innovative solutions for ergonomic problem analysis and prevention in surgical practice.
Purpose of the Study:
- To expand on existing ergonomic analysis of the lead surgeon during RAS.
- To develop predictive models for preventing ergonomic risk situations.
Main Methods:
- Four robotic-assisted tasks performed by surgeons with varying experience levels.
- Wearable technologies used to record posture and muscle activity.
- Correlation analysis between posture and muscle activity, with AI model generation.
Main Results:
- Positive correlation found between inadequate posture and increased muscle activation during RAS.
- Novice surgeons demonstrated better posture than expert surgeons.
- Predictive models achieved high accuracy for specific surgical tasks.
Conclusions:
- Surgeon's experience level significantly influences posture and muscle loading during RAS.
- Developed predictive models can aid in the prevention of ergonomic risks.
- Study enhances understanding of lead surgeon ergonomics in RAS.
