Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning

Liyun Gong; Miao Yu; Saeid Pourroostaei Ardakani

doi:10.4108/eetpht.11.9109

Authors

Liyun Gong University of Lincoln
Miao Yu University of Lincoln
Saeid Pourroostaei Ardakani University of Lincoln

DOI:

https://doi.org/10.4108/eetpht.11.9109

Keywords:

Federated Learning, Attention Model, Gait Analysis, Healthcare

Abstract

Human Gait Analysis is crucial in healthcare applications, with numerous research works focusing on machine learning and deep learning approaches for tasks such as abnormal gait detection and gait quality assessment. However, developing such models requires collecting and sharing a significant amount of patient data, raising privacy concerns. In this study, we introduce the world’s first technique for constructing a deep neural network model to stratify patients’ pain levels based on video recordings of timed up-and-go activities, while ensuring privacy preservation through modern federated learning algorithms. Our experimental results demonstrate the effectiveness of this technique in accurately stratifying LBP levels without the need for data sharing among local clients to maintain privacy.

Downloads

No metrics available.

-

see details

References

[1] Pirker, W, Katzenschlager, R.: Gait disorders in adults and the elderly: A clinical guide. Wien Klin Wochenschr, 129 (3), 81-95 (2017) DOI: https://doi.org/10.1007/s00508-016-1096-4

[2] The National Back Pain Pathway, NHS England, https://www.england.nhs.uk/blog/charlesgreenough/, last accessed 2024/07/11.

[3] Fatoye, F., Gebrye, T., Odeyemi, I.: Real-world incidence and prevalence of low back pain using routinely collected data, Rheumatol. Int., 39(4), 619–626, (2019). DOI: https://doi.org/10.1007/s00296-019-04273-0

[4] Hill, C., Dunn, K., Lewis, M., Mullis, R., Main, C., Foster, N., Hay, E.: A primary care back pain screening tool: Identifying patient subgroups for initial treatment, Arthritis Care Res.: Off. J. Am. Coll. Rheumatol., 59, 632-641 (2008). DOI: https://doi.org/10.1002/art.23563

[5] Abdollahi, M., Ashouri, S., Abedi, M., Azadeh-Fard, N., Parnianpour, M., Khalaf, K., Rashedi, E.: Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach, Sensors, 20(2), (2020). DOI: https://doi.org/10.3390/s20123600

[6] Molnar, M., Kok, M., Engel, T., Kaplick, H., Mayer, F., Seel, T.: A Method for Lower Back Motion Assessment Using Wearable 6D Inertial Sensors, 21st International Conference on Information Fusion (FUSION), Cambridge, UK (2018). DOI: https://doi.org/10.23919/ICIF.2018.8455828

[7] Villalba-Meneses, F. et al. : Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning, Sensors, 24(3), (2024). DOI: https://doi.org/10.3390/s24030831

[8] Keller, A., Torres-Espin, A., Peterson, T., Booker, J., O’Neil, C., Lotz, J., Bailey, J., Ferguson, A., Matthew, R.: Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain, Frontiers in Bioengineering and Biotechnology, 10, (2022). DOI: https://doi.org/10.3389/fbioe.2022.868684

[9] Hemming, R., Sheeran, L., Deursen R., Sparkes, V.: Non-specific chronic low back pain: differences in spinal kinematics in subgroups during functional tasks, Eur Spine J., 27(1), 163-170 (2018). DOI: https://doi.org/10.1007/s00586-017-5217-1

[10] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., Polosukhin, I.: Attention Is All You Need, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA (2017).

[11] McMahan, H., Moore, E., Ramage, D., Hampson, S., Arcas, B.: Communication-Efficient Learning of Deep Networks from Decentralized Data, https://arxiv.org/abs/1602.05629, last accessed 2024/07/11.

[12] Wu, Y., Kirillov, A., Massa, F., Lo, WY., Girshick, R.: Detectron2, https://github.com/facebookresearch/detectron2, last accessed 2024/07/11.

[13] McCall, S., Kolawole, S., Naz, A., Gong, L., Ahmed, S., Prasad, P., Yu, M., Wingate, J., Ardakani, S.: Computer Vision Based Transfer Learning-Aided Transformer Model for Fall Detection and Prediction, IEEE Access, 12 (2024). DOI: https://doi.org/10.1109/ACCESS.2024.3368065

[14] I. Loshchilov, I., Hutter, F.: Decoupled Weight Decay Regularization, International Conference on Learning Representations, Louisiana, United States (2019).