Early Detection of Sciatica Using Ensemble Learning Algorithms
DOI:
https://doi.org/10.68337/cpsm.v1.i1.2026-002Keywords:
Sciatica detection, wearable sensors, gait analysis, IMU, plantar pressure, random forest, sensor fusionAbstract
Sciatica is a musculoskeletal disorder caused by compression or irritation of the lumbar nerve roots. Gait and posture abnormalities that appear before clinical symptoms can be detected early by continuously monitoring biomechanical parameters such as pelvic movement and plantar pressure. This paper proposes an ensemble learning approach that uses a lumbar inertial measurement unit (IMU), which contains an accelerometer and a gyroscope, together with a multi-node plantar pressure insole. The sensor signals are fused, and engineered time-domain and statistical features are used by a random forest (RF) model to classify short windows as either normal or at risk. On a held-out test set of 250 windows, the model achieved an overall accuracy of 95%.
References
[1] Achanta Sampath Dakshina Murthy, Thangavel Karthikeyan, and R. Vinoth Kanna, "Gait-based person fall prediction using deep learning approach," Soft Comput., vol. 26, no. 23, pp. 12933-12941, 2022, doi: 10.1007/s00500-021-06125-1.
[2] Abdul Hadi Abdul Razak, Aladin Zayegh, Rezaul K. Begg, and Yufridin Wahab, "Foot plantar pressure measurement system: A review," Sensors, vol. 12, no. 7, pp. 9884-9912, 2012, doi: 10.3390/s120709884.
[3] Chathuri Senanayake and S. M. N. Arosha Senanayake, "A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation," Comput. Methods Biomech. Biomed. Eng., vol. 14, no. 10, pp. 863-874, 2011, doi: 10.1080/10255842.2010.499866.
[4] Yongsong Wang, Zhixin Li, Zhaohui Guo, Yin Ding, Zhan Huan, and Lin Chen, "A wearable gait assessment method for lumbar disc herniation based on adaptive Kalman filtering," arXiv:2312.09517, 2023, doi: 10.48550/arXiv.2312.09517.
[5] Miguel D. Sánchez Manchola, María J. Pinto Bernal, Marcela Munera, and Carlos A. Cifuentes, "Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals," Sensors, vol. 19, no. 13, Art. no. 2988, 2019, doi: 10.3390/s19132988.
[6] Syed Muhammad Razi Kazim Naqvi, Asma Iqbal, Sumbal Zahra Ameen, and Namra Afzal, "Acupressure based therapeutic smart shoe for developing countries," in Proc. 2022 24th Int. Multitopic Conf. (INMIC), 2022, pp. 1-6, doi: 10.1109/INMIC56986.2022.9972978.
[7] P. Revathi, R. Mouleeshuwarapprabu, K. Namitha, P. R. Prabhuraam, and V. Praveenkumar, "Development of IoT-enabled smart shoe for gait prediction and abnormality analysis," IET Conf. Proc., vol. 2023, no. 22, pp. 67-72, 2023, doi: 10.1049/icp.2023.2855.
[8] Zhonghe Guo, Yanzhang Li, Yuchen Wang, Haoxuan Liu, Rui Guo, Jingzhong Ma, Xiaoming Wu, Dong Jiang, and Tianling Ren, "Intelligent diagnosis and predictive rehabilitation assessment of chronic ankle instability using shoe-integrated sensor system," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 33, pp. 1978-1985, 2025, doi: 10.1109/TNSRE.2025.3563924.
Downloads
Published
Versions
- 2026-10-01 (2)
- 2026-09-30 (1)
Conference Proceedings Volume
Section
Categories
License
Copyright (c) 2026 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in Conference Proceedings in Science and Management are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright in their work and grant the journal first publication rights. Readers are free to share and adapt the published material for any purpose, including commercial use, provided appropriate credit is given to the author, a link to the license is provided, and any changes are indicated.
