Classifying Patient Handling Techniques to Reduce Risk of Musculoskeletal Injury in Nursing Students
Faculty Mentor(s)
Dr. Omofolakunmi Olagbemi, Computer Science; Dr. Brooke Odle, Engineering
Document Type
Poster
Event Date
4-12-2024
Abstract
Nurses suffer musculoskeletal injuries at a higher proportion than the general population due to physical strain and poor posture during patient-handling tasks; studies show that back injuries occur at a rate of 28.9 cases per 10,000 registered nurses. The purpose of this study is to apply multivariate time series classifiers (MTSCs) to classify six patient-handling tasks and the quality of subject posture (good, poor, or neutral) during these tasks. Manikins weighing 44 lbs, 66 lbs, and 110 lbs simulated patients. In this proof-of-concept study the XCM, ResNet, and MiniRocket MTSCs were trained with data collected from four non-nursing students using features from the data that were identified as most critical by the explainable classifier XCM. MiniRocket proved to be the most accurate classifier (96.2% model score). The next stage of this study will involve nursing students with varying degrees of experience in a simulated clinical environment.
Recommended Citation
Repository citation: Battaglia, Giovanni and Sanchez, Emanuel, "Classifying Patient Handling Techniques to Reduce Risk of Musculoskeletal Injury in Nursing Students" (2024). 23rd Annual A. Paul and Carol C. Schaap Celebration of Undergraduate Research and Creative Activity (2024). Paper 7.
https://digitalcommons.hope.edu/curca_23/7
April 12, 2024. Copyright © 2024 Hope College, Holland, Michigan.
Comments
This research was supported by the RESTORE Center of Stanford University, supported by NICHD of the National Institutes of Health under award number 5P2CHD101913, and in part by funding provided by the National Aeronautics and Space Administration (NASA), under award number 80NSSC20M0124, Michigan Space Grant Consortium (MSGC).