Assessing Musculoskeletal Disorder Susceptibility in Professional Drivers Using K-Means Algorithms

Abstract

The work of professional drivers is crucial in many economic sectors. Truck and bus drivers, taxi drivers, and delivery vehicle drivers are at the heart of the action, transporting goods and people to keep businesses running and ensure they reach their daily destinations. Behind this essential activity, significant challenges arise from working conditions and their impact on health. Because of that, driversare exposed to different risk factors, possibly contributing to the onset of musculoskeletal disorders (MSDs), such as low back pain and other symptoms. Various factors were identified, including exposure to car vibrations, long hours of sitting while driving, repetitive manual activities, psychosocial factors,and individual characteristics, which contribute to the development of these problems in these professionals. This paper proposes a driver profiling model using the K-means clustering algorithm to establish risk profiles associated with the occurrence of MSDs. The model involves integrating personal and professional variables to identify the most vulnerable. The model estimates suggest that only 21% of drivers are at low risk of developing MSDs, highlighting the high prevalence of these disorders within this occupation.