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Scalogram-Based Gait Abnormalities Classification Using Deep Convolutional Networks for Neurological and Non-Neurological Disorders

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Abstract

Purpose

In the present day, there is a steep increase in cases of neurological and non-neurological diseases that may affect a person’s normal gait. This study proposes a methodology for classifying gait abnormalities using convolutional neural networks based on scalogram analysis of electromyography and foot insole data.

Methods

This study utilizes scalograms for classifying electromyography and foot-insole data, offering robust handling of high-noise data with sudden transitions. The electromyography data is sourced from patients with hemiplegia, rheumatoid arthritis, prolapsed intervertebral disc, and osteoarthritis. Foot insole data from PhysioNet includes subjects with Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis. Classification tasks are performed using convolutional neural network architecture, with performance assessed across three optimizers—adam, stochastic gradient descent, and rmsprop—and employing both max and average pooling layers for enhanced model optimization.

Results

The results indicate promising classification performance, with an accuracy of 96.75% achieved using the adam optimizer with max pooling layer for overall classification. For the four-class classification task, 96.62% and 96.96% accuracy were attained using adam and rmsprop optimizers with max-pooling layers, respectively.

Conclusion

The study demonstrates that scalogram-based analysis, coupled with CNN classification, provides a robust framework for accurate and reliable diagnosis of gait abnormalities. The methodology shows significant promise in differentiating between different types of gait disorders, offering potential applications in clinical settings for reliable and accurate diagnosis of gait abnormalities.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Volpe, R. G. (1988). Alterations of gait in neuromuscular disease. Clinics in Podiatric Medicine and Surgery, 5(3), 627–638.

    Article  CAS  PubMed  Google Scholar 

  2. Harris, G. F., & Wertsch, J. J. (1994). Procedures for gait analysis. Archives of Physical Medicine and Rehabilitation, 75(2), 216–225.

    Article  CAS  PubMed  Google Scholar 

  3. Baker, R. (2006). Gait analysis methods in rehabilitation. Journal of neuroengineering and rehabilitation, 3(1), 1–10.

    Article  Google Scholar 

  4. Whittle, M. W. (1996). Clinical gait analysis: A review. Human movement science, 15(3), 369–387.

    Article  Google Scholar 

  5. Ganz, D. A., Bao, Y., Shekelle, P. G., & Rubenstein, L. Z. (2007). Will my patient fall? JAMA, 297(1), 77–86.

    Article  PubMed  Google Scholar 

  6. Alexander, N. B. (1996). Differential diagnosis of gait disorders in older adults. Clinics in geriatric medicine, 12(4), 689–703.

    Article  CAS  PubMed  Google Scholar 

  7. Verghese, J., LeValley, A., Hall, C. B., Katz, M. J., Ambrose, A. F., & Lipton, R. B. (2006). Epidemiology of gait disorders in community-residing older adults. Journal of the American Geriatrics Society, 54(2), 255–261.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Pirker, W., & Katzenschlager, R. (2017). Gait disorders in adults and the elderly. Wiener Klinische Wochenschrift, 129(3), 81–95.

    Article  PubMed  Google Scholar 

  9. You, Y. Y., & Chung, S. H. (2015). The effects of gait velocity on the gait characteristics of hemiplegic patients. Journal of physical therapy science, 27(3), 921–924.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hesse, S., Reiter, F., Jahnke, M., Dawson, M., Sarkodie-Gyan, T., & Mauritz, K. H. (1997). Asymmetry of gait initiation in hemiparetic stroke subjects. Archives of physical medicine and rehabilitation, 78(7), 719–724.

    Article  CAS  PubMed  Google Scholar 

  11. McGee, S. (2021). Evidence-based physical diagnosis e-book. Elsevier Health Sciences.

    Google Scholar 

  12. Kelsey, J. L., Golden, A. L., & Mundt, D. J. (1990). Low back pain/prolapsed lumbar intervertebral disc. Rheumatic Disease Clinics of North America, 16(3), 699–716.

    Article  CAS  PubMed  Google Scholar 

  13. Humphreys, S. C., & Eck, J. C. (1999). Clinical evaluation and treatment options for herniated lumbar disc. American family physician, 59(3), 575.

    CAS  PubMed  Google Scholar 

  14. McInnes, I. B., & Schett, G. (2011). The pathogenesis of rheumatoid arthritis. New England Journal of Medicine, 365(23), 2205–2219.

    Article  CAS  PubMed  Google Scholar 

  15. Baan, H., Dubbeldam, R., Nene, A. V., & van de Laar, M. A. (2012). Gait analysis of the lower limb in patients with rheumatoid arthritis: a systematic review. In Seminars in arthritis and rheumatism (Vol. 41, No. 6, pp. 768–788). WB Saunders.

  16. Broström, E. W., Esbjörnsson, A. C., von Heideken, J., & Iversen, M. D. (2012). Gait deviations in individuals with inflammatory joint diseases and osteoarthritis and the usage of three-dimensional gait analysis. Best Practice & Research Clinical Rheumatology, 26(3), 409–422.

    Article  Google Scholar 

  17. Hamerman, D. (1989). The biology of osteoarthritis. New England Journal of Medicine, 320(20), 1322–1330.

    Article  CAS  PubMed  Google Scholar 

  18. Mirelman, A., Bonato, P., Camicioli, R., Ellis, T. D., Giladi, N., Hamilton, J. L., Hass, C. J., Hausdorff, J. M., Pelosin, E., & Almeida, Q. J. (2019). Gait impairments in Parkinson’s disease. The Lancet Neurology, 18(7), 697–708.

    Article  PubMed  Google Scholar 

  19. Grabli, D., Karachi, C., Welter, M. L., Lau, B., Hirsch, E. C., Vidailhet, M., & François, C. (2012). Normal and pathological gait: What we learn from Parkinson’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 83(10), 979–985.

    Article  Google Scholar 

  20. Vuong, K., Canning, C. G., Menant, J. C., & Loy, C. T. (2018). Gait, balance, and falls in Huntington disease. Handbook of clinical neurology, 159, 251–260.

    Article  PubMed  Google Scholar 

  21. Moon, Y., Sung, J., An, R., Hernandez, M. E., & Sosnoff, J. J. (2016). Gait variability in people with neurological disorders: A systematic review and meta-analysis. Human movement science, 47, 197–208.

    Article  PubMed  Google Scholar 

  22. Singh, R. E., Iqbal, K., White, G. & Holtz, J. K. (2019). A review of EMG techniques for detection of gait disorders. Artif. Intell.-Appl. Med. Biol.

  23. Konrad, P. (2005). The ABC of EMG: A practical introduction to kinesiological electromyography.

  24. Subramaniam, S., Majumder, S., Faisal, A. I., & Deen, M. J. (2022). Insole-based systems for health monitoring: Current solutions and research challenges. Sensors, 22(2), 438.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Reiz, R., & Morgos, L. (2013). IF estimation using spectrogram and scalogram. Journal of Electrical and Electronics Engineering, 6(2), 25.

    Google Scholar 

  26. Popescu, N., Channa, A., & Ifrim, R., Neuro-cognitive Evaluations using Deep Learning and Wearable Sensorial Devices. New Approaches, p.26.

  27. Le, H. T., Phung, S. L., & Bouzerdoum, A. (2018). August. Human gait recognition with micro-Doppler radar and deep autoencoder. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 3347–3352). IEEE.

  28. Oh, D. C., & Jo, Y. U. (2021). Classification of hand gestures based on multi-channel EMG by scale average wavelet transform and convolutional neural network. International Journal of Control, Automation and Systems, 19(3), 1443–1450.

    Article  Google Scholar 

  29. Jeong, H. K., An, S., Herrin, K., Scherpereel, K., Young, A., & Inan, O. T. (2021). Quantifying asymmetry between medial and lateral compartment knee loading forces using acoustic emissions. IEEE Transactions on Biomedical Engineering, 69(4), 1541–1551.

    Article  Google Scholar 

  30. Lin, C. J., & Lukodono, R. P. (2021). Sustainable human–robot collaboration based on human intention classification. Sustainability, 13(11), 5990.

    Article  Google Scholar 

  31. Badura, A., Masłowska, A., Myśliwiec, A., & Piętka, E. (2021). Multimodal signal analysis for pain recognition in physiotherapy using wavelet scattering transform. Sensors, 21(4), 1311.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Enders, H., & Nigg, B. M. (2016). Measuring human locomotor control using EMG and EEG: Current knowledge, limitations and future considerations. European journal of sport science, 16(4), 416–426.

    Article  PubMed  Google Scholar 

  33. Dimitrova, N. A., & Dimitrov, G. V. (2003). Interpretation of EMG changes with fatigue: Facts, pitfalls, and fallacies. Journal of Electromyography and Kinesiology, 13(1), 13–36.

    Article  CAS  PubMed  Google Scholar 

  34. Uribe, J. S., Vale, F. L., & Dakwar, E. (2010). Electromyographic monitoring and its anatomical implications in minimally invasive spine surgery. Spine, 35(26S), S368–S374.

    Article  PubMed  Google Scholar 

  35. Boyer, M., Bouyer, L., Roy, J. S., & Campeau-Lecours, A. (2023). Reducing noise, artifacts and interference in single-channel EMG signals: A review. Sensors, 23(6), 2927.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Day, S. (2002). Important factors in surface EMG measurement (pp. 1–17). London: Bortec Biomedical Ltd publishers.

    Google Scholar 

  37. Solomonow, M., Baratta, R., Bernardi, M., Zhou, B., Lu, Y., Zhu, M., & Acierno, S. (1994). Surface and wire EMG cross-talk in neighbouring muscles. Journal of Electromyography and Kinesiology, 4(3), 131–142.

    Article  CAS  PubMed  Google Scholar 

  38. Mesin, L. (2018). Optimal spatio-temporal filter for the reduction of cross-talk in surface electromyogram. Journal of neural engineering, 15(1), 016013.

    Article  PubMed  Google Scholar 

  39. Talib, I., Sundaraj, K., Lam, C. K., Hussain, J., & Ali, M. A. (2019). A review on cross-talk in myographic signals. European journal of applied physiology, 119, 9–28.

    Article  PubMed  Google Scholar 

  40. Winter, D. A., & Yack, H. J. (1987). EMG profiles during normal human walking: Stride-to-stride and inter-subject variability. Electroencephalography and clinical neurophysiology, 67(5), 402–411.

    Article  CAS  PubMed  Google Scholar 

  41. Guidetti, L., Rivellini, G., & Figura, F. (1996). EMG patterns during running: Intra-and inter-individual variability. Journal of Electromyography and Kinesiology, 6(1), 37–48.

    Article  CAS  PubMed  Google Scholar 

  42. Hug, F. (2011). Can muscle coordination be precisely studied by surface electromyography? Journal of electromyography and kinesiology, 21(1), 1–12.

    Article  PubMed  Google Scholar 

  43. Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 11(2), 45.

    Article  Google Scholar 

  44. Di Nardo, F., Basili, T., Meletani, S., & Scaradozzi, D. (2022). Wavelet-based assessment of the muscle-activation frequency range by EMG analysis. IEEE Access, 10, 9793–9805.

    Article  Google Scholar 

  45. Romanato, M., Strazza, A., Piatkowska, W. J., Spolaor, F., Fioretti, S., Volpe, D., Sawacha, Z., & Di Nardo, F. (2021). June. Characterization of EMG time-frequency content during Parkinson walking: a pilot study. In 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1–6). IEEE.

  46. Veer, K., & Agarwal, R. (2014). Wavelet denoising and evaluation of electromyogram signal using statistical algorithm. International Journal of Biomedical Engineering and Technology, 16(4), 293–305.

    Article  Google Scholar 

  47. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., Mietus, J. E., Moody, G. B., Peng, C. K., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.

    Article  CAS  PubMed  Google Scholar 

  48. Hausdorff, J. M., Lertratanakul, A., Cudkowicz, M. E., Peterson, A. L., Kaliton, D., & Goldberger, A. L. (2000). Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. Journal of applied physiology., 88(6), 2045–2053.

    Article  CAS  PubMed  Google Scholar 

  49. Frenkel-Toledo, S., Giladi, N., Peretz, C., Herman, T., Gruendlinger, L., & Hausdorff, J. M. (2005). Effect of gait speed on gait rhythmicity in Parkinson’s disease: Variability of stride time and swing time respond differently. Journal of neuroengineering and rehabilitation, 2(1), 1–7.

    Article  Google Scholar 

  50. Arnal, A. (2014). XXIII congreso de ecuaciones diferenciales y aplicaciones: XIII congreso de matemática aplicada. In XXIII congreso de ecuaciones diferenciales y aplicaciones (pp. 1–1051). Universitat Jaume I.

  51. Li, Z., Liu, F., Yang, W., Peng, S. & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.

  52. Kim, P. (2017). Convolutional neural network. In MATLAB deep learning (pp. 121–147). Apress, Berkeley, CA.

  53. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprintarXiv:1603.04467.

  54. Kumar, M., Gautam, D. P., & Bhaskar, D. V. (2022). Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders. IJEER, 10(2), 117–121.

    Article  Google Scholar 

  55. Negi, S., Negi, P. C., Sharma, S., & Sharma, N. (2020). Human locomotion classification for different terrains using machine learning techniques. Critical Reviews™ in Biomedical Engineering, 48(4), 199–209.

    Article  PubMed  Google Scholar 

  56. Morbidoni, C., Cucchiarelli, A., Fioretti, S., & Di Nardo, F. (2019). A deep learning approach to EMG-based classification of gait phases during level ground walking. Electronics, 8(8), 894.

    Article  Google Scholar 

  57. Guo, Y., Wu, X., Shen, L., Zhang, Z., & Zhang, Y. (2019). Method of gait disorders in Parkinson’s disease classification based on machine learning algorithms. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 768–772). IEEE.

  58. Yu, J., Park, S., Kwon, S. H., Ho, C. M. B., Pyo, C. S., & Lee, H. (2020). AI-based stroke disease prediction system using real-time electromyography signals. Applied Sciences, 10(19), 6791.

    Article  CAS  Google Scholar 

  59. Park, S. J., Hussain, I., Hong, S., Kim, D., Park, H., & Benjamin, H. C. M. (2020). Real-time gait monitoring system for consumer stroke prediction service. In 2020 IEEE International conference on consumer electronics (ICCE) (pp. 1–4). IEEE.

  60. Arumugaraja, M., Padmapriya, B., & Poornachandra, S. (2022). Design and development of foot worn piezoresistive sensor for knee pain analysis with supervised machine learning algorithms based on gait pattern. Measurement, 200, 111603.

    Article  Google Scholar 

  61. Shalin, G., Pardoel, S., Nantel, J., Lemaire, E. D., & Kofman, J. (2020). Prediction of freezing of gait in Parkinson’s disease from foot plantar-pressure arrays using a convolutional neural network. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 244–247). IEEE.

  62. Turner, A., & Hayes, S. (2019). The classification of minor gait alterations using wearable sensors and deep learning. IEEE Transactions on Biomedical Engineering, 66(11), 3136–3145.

    Article  PubMed  Google Scholar 

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Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Research Scholar, School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Uttar Pradesh, Varanasi, India

    Pranshu C. B. S. Negi

  2. Department of Orthopaedics, Institute of Medical Sciences (Banaras Hindu University), Uttar Pradesh, Varanasi, India

    S. S. Pandey

  3. School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Uttar Pradesh, Varanasi, India

    Shiru Sharma

  4. School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Uttar Pradesh, Varanasi, India

    Neeraj Sharma

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  1. Pranshu C. B. S. Negi
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Contributions

All authors contributed to the study conception and design All authors read and approved the final manuscript.

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Correspondence to Pranshu C. B. S. Negi.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study is approved by the Institute Ethics Committee, Institute of Medical Science (Banaras Hindu University).

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Informed consent was obtained from all individual participants included in the study.

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Informed consent was obtained from all individual participants to publish the data in the study.

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Negi, P.C.B.S., Pandey, S.S., Sharma, S. et al. Scalogram-Based Gait Abnormalities Classification Using Deep Convolutional Networks for Neurological and Non-Neurological Disorders. J. Med. Biol. Eng. (2024). https://doi.org/10.1007/s40846-024-00864-w

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