Quantifying Activity of the Erector Spinae Longissimus Subgroup Using EMG During Prolonged Driving
DOI:
https://doi.org/10.31436/iiumej.v27i2.3991Keywords:
prolonged driving, muscle fatigue, low back pain, emgAbstract
Prolonged driving has been associated with localized muscle fatigue, particularly in the lumbar region, posing safety and ergonomic concerns. This study investigates the dynamics of multivariate surface electromyography (sEMG) features to detect fatigue during extended driving sessions. EMG signals were recorded from the erector spinae muscles of seven subjects across two backrest inclination angles (100°, 110°) during real-world, multi-hour driving tasks. Spectrogram-based features, including mean frequency (MNF), median frequency (MDF), and root-mean-square (RMS), were extracted using a sliding-window method for analysis. Co-temporal trends in these features were analyzed to identify fatigue regions, marked by simultaneous decreases in MNF and MDF and/or increases in RMS. Paired t-tests were used to compare the two backrest inclination angles across subjects for each fatigue duration and fatigue event count. Results demonstrate that seat inclination angle significantly affects fatigue duration and number of events, with the 100° angle leading to notably more fatigue than 110° (p<0.01). This work could contribute a framework for time-resolved, EMG fatigue modeling in realistic driving scenarios.
ABSTRAK: Pemanduan terlalu lama dikaitkan dengan keletihan otot setempat, khususnya di bahagian lumbar, menimbulkan kebimbangan dari segi keselamatan dan ergonomik. Kajian ini mengkaji dinamika ciri permukaan multivarian elektromiografi (sEMG) bagi mengesan keletihan semasa sesi pemanduan jauh. Isyarat EMG direkodkan daripada otot erektor spina dari 7 subjek pada dua sudut kecondongan penyandar (100°, 110°) semasa aktiviti pemanduan sebenar berlangsung selama beberapa jam. Ciri berasaskan spektrogram, termasuk frekuensi purata (MNF), frekuensi median (MDF) dan purata punca kuasa dua (RMS) telah diekstrak menggunakan tetingkap-gelongsor. Corak serentak dalam ciri-ciri ini dianalisa bagi mengenal pasti kawasan keletihan, dibuktikan dengan penurunan MNF dan MDF serta/atau peningkatan RMS. Ujian-t berkembar digunakan bagi membandingkan dua sudut kecondongan penyandar merentas subjek bagi setiap tempoh keletihan dan bilangan kejadian. Dapatan kajian menunjukkan bahawa sudut kecondongan penyandar memberi kesan signifikan terhadap tempoh keletihan dan bilangan kejadian, dengan sudut 100° menghasilkan lebih banyak keletihan berbanding 110° (p<0.01). Kajian ini berpotensi menyumbang kepada satu rangka kerja pada model keletihan EMG beresolusi masa dalam senario pemanduan realistik.
Downloads
References
Wan JJ, Qin Z, Wang PY, Sun Y, Liu X (2017). Muscle fatigue: General understanding and treatment. Experimental & Molecular Medicine, 49(10):e384. https://doi.org/10.1038/emm.2017.194
Chen C, Xiao B, He X, Wu J, Li W, Yan M (2024). Prevalence of low back pain in professional drivers: A meta-analysis. Public Health, 231:23–30. https://doi.org/10.1016/j.puhe.2024.03.007
Roldán Jiménez C, Bennett P, Ortiz García A, Cuesta Vargas AI (2019). Fatigue detection during sit-to-stand test based on surface electromyography and acceleration: A case study. Sensors, 19(19):4202. https://doi.org/10.3390/s19194202
Mader L, Herzberg M, Anders C (2024). Reliability of sEMG data of back muscles during static submaximal loading situations: Values and pitfalls. Journal of Electromyography and Kinesiology, 79:102947. https://doi.org/10.1016/j.jelekin.2024.102947
Phinyomark A, Thongpanja S, Hu H, Phukpattaranont P, Limsakul C (2012). The usefulness of mean and median frequencies in electromyography analysis. Computational intelligence in electromyography analysis: a perspective on current applications and future challenges, pp 195. https://doi.org/10.5772/50639.
Lecocq T, Chevrel C, Gorce P, Madeleine P (2022). Perceived discomfort and neuromuscular fatigue during long-duration real driving with different car seats. Frontiers in Sports and Active Living, 4:990748. https://doi.org/10.3389/fspor.2022.990748
Azmi NL, Ghafar NAA, Nor KAM, Nordin NHD (2022). Classification of muscle fatigue during prolonged driving. ELEKTRIKA: Journal of Electrical Engineering, 21(3):40–46. https://doi.org/10.11113/elektrika.v21n3.376
Tao X, Cheng B, Wang B, Zhang F, Li G, Chen C (2013). SEMG based recognition for lumbar muscle fatigue during prolonged driving. In Proceedings of the FISITA 2012 World Automotive Congress: Vehicle Design and Testing (I), 7:773–783.
Menotti F, Labanca L, Laudani L, Giombini A, Pigozzi F, Macaluso A (2015). Activation of neck and low-back muscles is reduced with the use of a neck balance system together with a lumbar support in urban drivers. PLoS ONE, 10(10):e0141031. https://doi.org/10.1371/journal.pone.0141031
Eltayeb MA, Azmi NL, Md Nor KA, Toha SF, Hashim A, Nordin NHD (2025). sEMG analysis of muscle fatigue during prolonged driving. PERINTIS EJournal, 15(1):15–21. Retrieved from https://perintis.org.my/ejournalperintis/index.php/PeJ/article/view/179
Katsis CD, Ntouvas NE, Bafas CG, Fotiadis DI. (2004) Assessment of muscle fatigue during driving using surface EMG. Proceedings of the 2nd IASTED International Conference on Biomedical Engineering, 259–262.
Hermens HJ, Freriks B, Merletti R, Stegeman D, Blok J, Rau G, Hägg G (1999). European recommendations for surface electromyography: Results of the SENIAM project. Roessingh Research and Development. Retrieved from http://seniam.org/
De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH (2010). Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics, 43(8):1573–1579. https://doi.org/10.1016/j.jbiomech.2010.01.027
Ng CL, Reaz MBI, Ali SHBM, Crespo ML, Cicuttin A, Chowdhury MEH, Kiranyaz S, Kamal NB (2023). Powerline interference suppression of a textile-insulated capacitive biomedical sensor using digital filters. Measurement, 207:112425. https://doi.org/10.1016/j.measurement.2022.112425
Butler HL, Hubley-Kozey CL, Kozey JW (2009). Electromyographic assessment of trunk muscle activation amplitudes during a simulated lifting task using pattern recognition techniques. Journal of Electromyography and Kinesiology, 19(6):e505–e512. https://doi.org/10.1016/j.jelekin.2008.09.010
Conwit RA, Stashuk D, Suzuki H, Lynch N, Schrager M, Metter EJ (2000). Fatigue effects on motor unit activity during submaximal contractions. Archives of Physical Medicine and Rehabilitation, 81(9):1211–1216. https://doi.org/10.1053/apmr.2000.6975
Song J, Choi YS, Lee S, Park D, Park J (2025). Changes in muscle oxygenation and activity during cumulative isometric muscle contraction: New insight into muscle fatigue. Frontiers in Physiology, 16:1559893. https://doi.org/10.3389/fphys.2025.1559893
Majid NAA, Abdullah MFE, Jamaludin MS, Notomi M, Rasmussen J (2013). Musculoskeletal analysis of driving fatigue: The influence of seat adjustments. Advanced Engineering Forum, 10:373–378. https://doi.org/10.4028/www.scientific.net/aef.10.373
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 IIUM Press

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.








