Alizadeh, M. and M. Gheitasi, Fundamental Concepts of Corrective Exercises. 3st Publish. Sport
Science Research Center, 2019. 23(2): p. 148-163.
2. Janyathitipath, T., et al., Responsiveness of Lumbar Lordosis Angle and Other Biomechanical
Parameters in Individuals with Lumbar Hyperlordosis: An Experimental Study. Srinagarind Medical Journal,
2020. 35(4): p. 470-475.
3. Scannell, J.P. and S.M. McGill, Lumbar posture—should it, and can it, be modified? A study of
passive tissue stiffness and lumbar position during activities of daily living. Physical Therapy, 2003. 83(10):
p. 907-917. https://doi.org/10.1093/ptj/83.10.907
4. Been, E. and L. Kalichman, Lumbar lordosis. The Spine Journal, 2014. 14(1): p. 87-97.
10.1016/j.spinee.2013.07.464
5. Clark, M. and S. Lucett, NASM essentials of corrective exercise training. 2010: Lippincott Williams
& Wilkins. 0781768020, 9780781768023
6. Farhadi, M.H.I., et al., Differences in Gluteal and Quadriceps Muscle Activation Among Adults
With and Without Lumbar Hyperlordosis. Journal of sport rehabilitation, 2020. 29(8): p. 1100-1105.
10.1123/jsr.2019-0112
7. Fatemi, R., M. Javid, and E.M. Najafabadi, Effects of William training on lumbosacral muscles
function, lumbar curve and pain. Journal of back and musculoskeletal rehabilitation, 2015. 28(3): p. 591-597.
10.3233/BMR-150585
8. Youdas, J.W., et al., Lumbar lordosis and pelvic inclination in adults with chronic low back pain.
Physical therapy, 2000. 80(3): p. 261-275. 10696153https://doi.org/10.1093/ptj/80.3.261
9. Jiang, N., K.D.-K. Luk, and Y. Hu, A machine learning-based surface electromyography topography
evaluation for prognostic prediction of functional restoration rehabilitation in chronic low back pain. Spine,
2017. 42(21): p. 1635-1642. 10.1097/BRS.0000000000002159
10. Saranya, S., et al., EMG Features as an Indicator of Muscle Strength for the Assessment of NonSpecific Low Back Pain, in Assistive Technology Intervention in Healthcare. 2022, CRC Press. p. 177-188.
9781003207856
11. Piatkowska, W., et al., EMG analysis across different tasks improves prevention screenings in
diabetes: a cluster analysis approach. Medical & biological engineering & computing, 2022. 60(6): p. 1659-
1673. https://doi.org/10.1016/j.gaitpost.2020.08.031
12. Di Nardo, F., et al., Machine learning for detection of muscular activity from surface EMG signals.
Sensors, 2022. 22(9): p. 3393. https://doi.org/10.3390/s22093393
13. Rosati, S., et al., Muscle activation patterns during gait: A hierarchical clustering analysis.
Biomedical Signal Processing and Control, 2017. 31: p. 463-469. https://doi.org/10.1016/j.bspc.2016.09.017
14. White, S.G. and P.J. McNair, Abdominal and erector spinae muscle activity during gait: the use of
cluster analysis to identify patterns of activity. Clinical Biomechanics, 2002. 17(3): p. 177-184.
https://doi.org/10.1016/S0268-0033(02)00007-4
15. Hodges, P.W. and C.A. Richardson, Inefficient muscular stabilization of the lumbar spine
associated with low back pain: a motor control evaluation of transversus abdominis. Spine, 1996. 21(22): p.
2640-2650.
16. D’Antoni, F., et al., Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back
Pain: A Systematic Review. International Journal of Environmental Research and Public Health, 2022.
19(10): p. 5971. https://doi.org/10.3390/ijerph19105971
17. Nazmi, N., et al., Walking gait event detection based on electromyography signals using artificial
neural network. Biomedical Signal Processing and Control, 2019. 47: p. 334-343.
https://doi.org/10.1016/j.bspc.2018.08.030
18. Youdas, J.W., J.H. Hollman, and D.A. Krause, The effects of gender, age, and body mass index on
standing lumbar curvature in persons without current low back pain. Physiotherapy theory and practice,
2006. 22(5): p. 229-237. https://doi.org/10.1080/09593980600927864
19. Puntumetakul, R., et al., The relationships between gender body mass index and lumbar spinal
curvature in standing position using the flexible ruler. Journal of Medical Technology and Physical Therapy,
2001. 13(1): p. 20-29. https://doi.org/10.14456/arch-ahs
20. Bhise, S.A. and N.K. Patil, Dominant and non-dominant leg activities in young adults. International
Journal of Therapies and Rehabilitation Research, 2016. 5(4): p. 257-64. https://doi:10.5455/ijtrr.000000172
21. Hermens, H.J., et al., Development of recommendations for SEMG sensors and sensor placement
procedures. Journal of electromyography and Kinesiology, 2000. 10(5): p. 361-374.
https://doi.org/10.1016/S1050-6411(00)00027-4
22. Cruz-Montecinos, C., et al., Changes in muscle activity patterns and joint kinematics during gait in
hemophilic arthropathy. Frontiers in physiology, 2020. 10: p. 1575. https://doi: 10.1088/0967-
3334/34/8/N63
23. Tirosh, O. and W. Sparrow, Age and walking speed effects on muscle recruitment in gait
termination. Gait & Posture, 2005. 21(3): p. 279-288. https://doi.org/10.1016/j.gaitpost.2004.03.002
24. Balbinot, A. and G. Favieiro, A neuro-fuzzy system for characterization of arm movements.
Sensors, 2013. 13(2): p. 2613-2630. https://doi.org/10.3390/s130202613
25. Wayne, P.M., et al., Tai chi training’s effect on lower extremity muscle co-contraction during
single-and dual-task gait: cross-sectional and randomized trial studies. PloS one, 2021. 16(1): p. e0242963.
https://doi.org/10.1371/journal.pone.0242963
26. Elamvazuthi, I., et al., Electromyography (EMG) based classification of neuromuscular disorders
using multi-layer perceptron. Procedia Computer Science, 2015. 76: p. 223-228.
https://doi.org/10.1016/j.procs.2015.12.346
27. Agatonovic-Kustrin, S. and R. Beresford, Basic concepts of artificial neural network (ANN)
modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis,
2000. 22(5): p. 717-727. https://doi.org/10.1016/S0731-7085(99)00272-1
28. Khong, L.M., et al. Multi-layer perceptron training algorithms for pattern recognition of myoelectric
signals. in The 6th 2013 Biomedical Engineering International Conference. 2013. IEEE.
https://doi.org/10.1109/BMEiCon.2013.6687665
29. Majidzadeh Gorjani, O., et al., Human activity classification using multilayer perceptron. Sensors,
2021. 21(18): p. 6207. https://doi.org/10.3390/s21186207
30. Yang, J. and G. Yang, Modified convolutional neural network based on dropout and the stochastic
gradient descent optimizer. Algorithms, 2018. 11(3): p. 28. https://doi.org/10.3390/a11030028
31. Ying, X. An overview of overfitting and its solutions. in Journal of physics: Conference series. 2019. IOP
Publishing. https://doi.org/10.1088/1742-6596/1168/2/022022
32. Sawacha, Z., et al., A new classification of diabetic gait pattern based on cluster analysis of
biomechanical data. 2010, SAGE Publications Sage CA: Los Angeles, CA.
https://doi.org/10.1177/193229681000400511
33. Harb, H., A. Makhoul, and R. Couturier, An enhanced K-means and ANOVA-based clustering
approach for similarity aggregation in underwater wireless sensor networks. IEEE Sensors Journal, 2015.
15(10): p. 5483-5493. 10.1109/JSEN.2015.2443380
34. Kobayashi, Y., et al., Key joint kinematic characteristics of the gait of fallers identified by principal
component analysis. Journal of biomechanics, 2014. 47(10): p. 2424-2429.
https://doi.org/10.1016/j.jbiomech.2014.04.011
35. Wang, Y., et al. Neural network using for prediction spinal diseases. in Conference of Open
Innovations Association, FRUCT. 2019. FRUCT Oy. ISSN 2305-7254
36. Morbidoni, C., et al., A deep learning approach to EMG-based classification of gait phases during
level ground walking. Electronics, 2019. 8(8): p. 894. https://doi.org/10.3390/electronics8080894
37. Nazmi, N., et al. Generalization of ANN model in classifying stance and swing phases of gait using
EMG signals. in 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES). 2018.
IEEE. DOI:10.1109/IECBES.2018.8626626
38. Rao, A.Z., et al., Sensor fusion and machine learning for seated movement detection with trunk
orthosis. IEEE Access, 2024. 12: p. 41676-41687. DOI: 10.1109/ACCESS.2024.3377111
39. Rani, G.J. and M.F. Hashmi. Electromyography (EMG) Signal based Knee Abnormality Prediction
using XGBoost Machine Learning Algorithm. in 2023 IEEE 2nd International Conference on Industrial
Electronics: Developments & Applications (ICIDeA). 2023. IEEE.
DOI:10.1109/ICIDeA59866.2023.10295245
40. Hudson, K.R., The relationship of hip flexor muscle length to lumbar lordosis and pelvic tilt. 1992:
Touro College. DOI: 10.1093/ptj/67.4.512
41. Rhee, I., et al., Hip-spine syndrome in patients with spinal cord injuries: hyperlordosis associated
with severe hip flexion contracture. Frontiers in Pediatrics, 2021. 9: p. 646107.
https://doi.org/10.3389/fped.2021.646107
42. Seidi, F., et al., Lower limb muscle activation pattern in male soccer players with lumbar
hyperlordosis. Journal of Bodywork and Movement Therapies, 2023. 36: p. 263-269.
https://doi.org/10.1016/j.jbmt.2023.03.004
43. Liang, R., et al., Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using
Machine Learning Approaches. International Journal of Environmental Research and Public Health, 2022.
19(3): p. 1177. https://doi.org/10.3390/ijerph19031177
44. Bergil, E., C. Oral, and E.U. Ergul, Efficient Hand Movement Detection Using k-Means Clustering
and k-Nearest Neighbor Algorithms. Journal of Medical and Biological Engineering, 2021. 41: p. 11-24.
https://doi.org/10.1007/s40846-020-00537-4
45. Rozumalski, A. and M.H. Schwartz, Crouch gait patterns defined using k-means cluster analysis are
related to underlying clinical pathology. Gait & posture, 2009. 30(2): p. 155-160.
https://doi.org/10.1016/j.gaitpost.2009.05.010
46. Yousefi, M.and E.A.H, Reliability of Body Landmark Analyzer (BLA) system for Measuring
Hyperkyphosis and Hyperlordosis Abnormalities. Journal of Advanced Sport Technology 4(1):20- 29.