ArticleJMIR formative research2025
Localized Muscular Fatigue in Robotic-Assisted Laparoscopic Surgery: Predictive Modeling Study.
Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundRobotic-assisted surgery (RAS) has grown rapidly in recent decades, and several RAS procedures have become the standard. However, the physical and mental demands of minimally invasive surgery (MIS) techniques can lead to ergonomic shortcomings for surgeons. Advances in wearable technology and artificial intelligence favor the development of innovative solutions to analyze and improve ergonomic conditions during surgical practice.
objectiveThe main objective is the development and validation of a predictive model of localized muscle fatigue from electromyography (EMG) data during conventional laparoscopic surgery (LAP) and RAS.
methodsFour different tasks were performed on LAP and RAS: dissection, labyrinth, peg transfer, and suturing. A wireless EMG sensor system was used to record muscle activity. Joint analysis of the spectrum and analysis graphs was used to evaluate the localized muscle fatigue. A dataset was generated for each task as a function of surgeons' expertise level and surgical type. Each dataset was scaled as preprocessing and divided into 2 datasets: 80% for training and 20% for testing. Multiple linear regression (MLR) and multilayer perceptron (MLP) were applied as predictive techniques and validated on all test datasets. R
resultsRAS showed less muscle fatigue for novice surgeons compared to LAP practice, although it was higher for expert surgeons. The predictive model achieved satisfactory R
conclusionsWearable technology and artificial intelligence techniques have been successfully applied for the development and validation of a novel predictive model based on MLR and MLP to predict localized muscle fatigue in MIS.
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