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, drivers
are 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.
Full Text:
PDFReferences
S. B. M. Tamrin, K. Yokoyama, N. Aziz, and S. Maeda, « Association of Risk Factors with Musculoskeletal Disorders among Male Commercial Bus Drivers in Malaysia », Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 24, no 4, p. 369‑385, 2014, doi: 10.1002/hfm.20387.
L. Montoro, S. Useche, F. Alonso, and B. Cendales, « Work Environment, Stress, and Driving Anger: A Structural Equation Model for Predicting Traffic Sanctions of Public Transport Drivers », International Journal of Environmental Research and Public Health, vol. 15, no 3, Art. no 3, mars 2018, doi: 10.3390/ijerph15030497.
A. A. Rufa'i and al., « Prevalence and Risk Factors for Low Back Pain Among Professional Drivers in Kano, Nigeria », Archives of Environmental & Occupational Health, vol. 70, no 5, p. 251‑255, sept. 2015, doi: 10.1080/19338244.2013.845139.
O. Pickard, P. Burton, H. Yamada, B. Schram, E. F. D. Canetti, and R. Orr, « Musculoskeletal Disorders Associated with Occupational Driving », Int J Environ Res Public Health, vol. 19, no 11, p. 6837, juin 2022, doi: 10.3390/ijerph19116837.
S. A. Arslan, M. R. Hadian, G. Olyaei, S. Talebian, M. S. Yekaninejad, and M. A. Hussain, « Comparative effect of driving side on low back pain due to Repetitive Ipsilateral Rotation », Pakistan Journal of Medical Sciences, vol. 35, no 4, p. 1018, août 2019, doi: 10.12669/pjms.35.4.488.
H. Ayari, M. Thomas, and S. Doré, « A Design of Experiments for Statistically Predicting Risk of Adverse Health Effects on Drivers Exposed to Vertical Vibrations », International Journal of Occupational Safety and Ergonomics, vol. 17, no 3, p. 221‑232, janv. 2011, doi: 10.1080/10803548.2011.11076888.
A. V. Araújo, G. S. Arcanjo, H. Fernandes, and G. S. Arcanjo, « Ergonomic work analysis: A case study of bus drivers in the private collective transportation sector », Work, vol. 60, no 1, p. 41‑47, janv. 2018, doi: 10.3233/WOR-182718.
S. Senthanar and P. L. Bigelow, « Factors associated with musculoskeletal pain and discomfort among Canadian truck drivers: A cross-sectional study of worker perspectives », Journal of Transport & Health, vol. 11, p. 244‑252, déc. 2018, doi: 10.1016/j.jth.2018.08.013.
M. Grabara, « The association between physical activity and musculoskeletal disorders—a cross-sectional study of teachers », PeerJ, vol. 11, p. e14872, févr. 2023, doi: 10.7717/peerj.14872.
R. Govaerts and al., « Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: a systematic review and meta-analysis », BMC Musculoskeletal Disorders, vol. 22, no 1, p. 751, août 2021, doi: 10.1186/s12891-021-04615-9.
L. M. Matos and al., « Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning », Engineering Applications of Artificial Intelligence, vol. 138, p. 109353, déc. 2024, doi: 10.1016/j.engappai.2024.109353.
J.-M. Su, J.-H. Chang, N. L. D. Indrayani, and C.-J. Wang, « Machine learning approach to determine the decision rules in ergonomic assessment of working posture in sewing machine operators », Journal of Safety Research, vol. 87, p. 15‑26, déc. 2023, doi: 10.1016/j.jsr.2023.08.008.
J. Zhao, E. Obonyo, and S. G. Bilén, « Wearable Inertial Measurement Unit Sensing System for Musculoskeletal Disorders Prevention in Construction », Sensors, vol. 21, no 4, Art. no 4, janv. 2021, doi: 10.3390/s21041324.
M. Cohen and al., « Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs », Eur Radiol, vol. 33, no 6, p. 3974‑3983, juin 2023, doi: 10.1007/s00330-022-09349-3.
H. Hess and al., « Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction », Diagnostics, vol. 13, no 10, Art. no 10, janv. 2023, doi: 10.3390/diagnostics13101668.
V. A. Georgeanu, M. Mămuleanu, S. Ghiea, and D. Selișteanu, « Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms », Medicina, vol. 58, no 5, Art. no 5, mai 2022, doi: 10.3390/medicina58050636.
W. Shiguang, « RETRACTED ARTICLE: Simulation of sports injury prevention and rehabilitation monitoring based on fiber optic sensors and machine learning algorithms », Opt Quant Electron, vol. 56, no 4, p. 616, févr. 2024, doi: 10.1007/s11082-024-06321-x.
A. Obukhov and al., « Examination of the Accuracy of Movement Tracking Systems for Monitoring Exercise for Musculoskeletal Rehabilitation », Sensors, vol. 23, no 19, Art. no 19, janv. 2023, doi: 10.3390/s23198058.
S. A. Balakrishnan, E. F. Sundarsingh, V. S. Ramalingam, and A. N, « Conformal Microwave Sensor for Enhanced Driving Posture Monitoring and Thermal Comfort in Automotive Sector », IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, p. 1‑8, 2024, doi: 10.1109/JERM.2024.3405185.
M. Aliabadi, E. Darvishi, M. Farhadian, R. Rahmani, M. Shafiee Motlagh, and N. Mahdavi, « An investigation of musculoskeletal discomforts among mining truck drivers with respect to human vibration and awkward body posture using random forest algorithm », Human Factors and Ergonomics In Manufacturing, vol. 32, no 6, p. 482‑493, nov. 2022, doi: 10.1002/hfm.20965.
M. E. Celebi and K. Aydin, Éd., Unsupervised Learning Algorithms. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-24211-8.
T. Kodinariya and P. Makwana, « Review on Determining of Cluster in K-means Clustering », International Journal of Advance Research in Computer Science and Management Studies, vol. 1, p. 90‑95, janv. 2013.
A. A. Abdulnassar and L. R. Nair, « Performance analysis of Kmeans with modified initial centroid selection algorithms and developed Kmeans9+ model », Measurement: Sensors, vol. 25, p. 100666, févr. 2023, doi: 10.1016/j.measen.2023.100666.
N. Hasheminejad, M. Amirmahani, and S. Tahernejad, « Biomechanical evaluation of midwifery tasks and its relationship with the prevalence of musculoskeletal disorders », Heliyon, vol. 9, no 9, sept. 2023, doi: 10.1016/j.heliyon.2023.e19442.
E. Rezaei, F. Shahmahmoudi, F. Makki, F. Salehinejad, H. Marzban, and Z. Zangiabadi, « Musculoskeletal disorders among taxi drivers: a systematic review and meta-analysis », BMC Musculoskelet Disord, vol. 25, no 1, p. 663, août 2024, doi: 10.1186/s12891-024-07771-w.
DOI: https://doi.org/10.31449/inf.v49i4.7561
This work is licensed under a Creative Commons Attribution 3.0 License.