Skip to main content
SpringerLink
Log in

Scoliosis Screening through a Machine Learning Based Gait Analysis Test

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

This study discussed application of a machine learning approach (Support vector machine, SVM) for the automatic cognition of gait changes due to scoliosis using gait measures: kinematic based on gait phase segmentation. The gaits of 18 controls and 24 scoliosis patients were recorded and analyzed using inertial measurement unit (IMU)-based systems during normal walking. Altogether, 72 gait features were extracted for developing gait recognition models. Cross-validation test results indicated that the performance of SVM was 90.5% to recognize scoliosis patients and controls gait patterns. When features were optimally selected, a feature selection algorithm could effectively distinguish the age groups with 95.2% accuracy. Applying the method that the previous test used, the severity of scoliosis was classified after clinician labeled the severity based on the Cobb angle. Test results indicated an accuracy of 81.0% by the SVM to recognize scoliosis severity gait patterns. Optimal selected features could effectively distinguish the scoliosis severity with 85.7% accuracy. When the measured features are ranked in order of high contribution, the abduction and adduction of left hip joint in the single support phase is most important in gait of patients with scoliosis. These results demonstrate considerable potential in applying SVMs in gait classification for medical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

SVM:

Support vector machine

IMU:

Inertial measurement unit

SP:

Stance phase

SW:

Swing phase

IDS:

Initial double support phase

SS:

Single support phase

TDS:

Terminal double support phase

References

  1. Lee, J.-H. and Kim, S.-Y., “Comparative Effectiveness of Schroth Therapeutic Exercise Versus Sling Therapeutic Exercise in Flexibility, Balance, Spine Angle and Chest Expansion in Patient WITH Scoliosis,” Journal of the Korean Society of Physical Medicine, Vol. 9, No. 1, pp. 11–23, 2014.

    Article  Google Scholar 

  2. Choi, A., Yun, T. S., Suh, S. W., Yang, J. H., Park, H., et al., “Determination of Input Variables for the Development of a Gait Asymmetry Expert System in Patients with Idiopathic Scoliosis,” International Journal of Precision Engineering and Manufacturing, Vol. 14, No. 5, pp. 811–818, 2013.

    Article  Google Scholar 

  3. Kramers-de Quervain, I. A., Müller, R., Stacoff, A., Grob, D., and Stüssi, E., “Gait Analysis in Patients with Idiopathic Scoliosis,” European Spine Journal, Vol. 13, No. 5, pp. 449–456, 2004.

    Article  Google Scholar 

  4. Kim, J., Oh, S.-I., Cho, H., Kim, H. S., Chon, J., et al., “Gait Patterns of Chronic Ambulatory Hemiplegic Elderly Compared with Normal Age-Matched Elderly,” International Journal of Precision Engineering and Manufacturing, Vol. 16, No. 2, pp. 385–392, 2015.

    Article  Google Scholar 

  5. Mahaudens, P., Detrembleur, C., Mousny, M., and Banse, X., “Gait in Adolescent Idiopathic Scoliosis: Energy Cost Analysis,” European Spine Journal, Vol. 18, No. 8, pp. 1160–1168, 2009.

    Article  Google Scholar 

  6. Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A. M., “A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients,” Sensors, Vol. 16, No. 1, Paper No. 134, 2016.

    Google Scholar 

  7. Tahir, N. M. and Manap, H. H., “Parkinson Disease Gait Classification Based on Machine Learning Approach,” Journal of Applied Sciences, Vol. 12, No. 2, pp. 180–185, 2012.

    Article  Google Scholar 

  8. Zavaljevski, N., Stevens, F. J., and Reifman, J., “Support Vector Machines with Selective Kernel Scaling for Protein Classification and Identification of Key Amino Acid Positions,” Bioinformatics Vol. 18, No. 5, pp. 689–96, 2002.

    Article  Google Scholar 

  9. Gunn, S. R., “Support Vector Machines for Classification and Regression,” ISIS Technical Report, University of Southampton, pp. 2–3, 1998.

    Google Scholar 

  10. Begg, R. and Kamruzzaman, J., “A Machine Learning Approach for Automated Recognition of Movement Patterns Using Basic, Kinetic and Kinematic Gait Data,” Journal of Biomechanics, Vol. 38, No. 3, pp. 401–408, 2005.

    Article  Google Scholar 

  11. LeMoyne, R., Mastroianni, T., Hessel, A., and Nishikawa, K., “Implementation of Machine Learning for Classifying Prosthesis Type through Conventional Gait Analysis,” Proc. of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 202–205, 2015.

    Google Scholar 

  12. Nishida, M., Nagura, T., Fujita, N., Hosogane, N., Tsuji, T., et al., “Position of the Major Curve Influences Asymmetrical Trunk Kinematics during Gait in Adolescent Idiopathic Scoliosis,” Gait & Posture, Vol. 51, pp. 142–148, 2017.

    Article  Google Scholar 

  13. Greene, B. R., McGrath, D., O’Neill, R., O’Donovan, K. J., Burns, A., and Caulfield, B., “An Adaptive Gyroscope-Based Algorithm for Temporal Gait Analysis,” Medical & Biological Engineering & Computing, Vol. 48, No. 12, pp. 1251–1260, 2010.

    Article  Google Scholar 

  14. Patterson, M. R., Delahunt, E., Sweeney, K. T., and Caulfield, B., “An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns,” Sensors, Vol. 14, No. 1, pp. 887–899, 2014.

    Article  Google Scholar 

  15. Salarian, A., Russmann, H., Vingerhoets, F. J., Dehollain, C., Blanc, Y., et al., “Gait Assessment in Parkinson's Disease: Toward an Ambulatory System for Long-Term Monitoring,” IEEE Transactions on Biomedical Engineering, Vol. 51, No. 8, pp. 1434–1443, 2004.

    Article  Google Scholar 

  16. Sabatini, A. M., Martelloni, C., Scapellato, S., and Cavallo, F., “Assessment of Walking Features from Foot Inertial Sensing,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 3, pp. 486–494, 2005.

    Article  Google Scholar 

  17. Raúl, F. A., Eduardo, Z. C., and Jaime, G. G.-B. “Pedestrian Tracking Using Inertial Sensors,” Journal of Physical Agents, Vol. 3, No. 1, pp. 35–34, 2009.

    Google Scholar 

  18. Lee, K. H., Kang, S. I., Cho, J. S., Lim, D. G., Lee, J. S., et al., “Development of Gait Distance Measurement System Based on Inertial Measurement Units,” Journal of Rehabilitation Welfare Engineering & Assistive Technology, Vol. 9, No. 2, pp. 161–168, 2015.

    Google Scholar 

  19. Brennan, A., Zhang, J., Deluzio, K., and Li, Q., “Quantification of Inertial Sensor-Based 3D Joint Angle Measurement Accuracy Using an Instrumented Gimbal,” Gait & Posture, Vol. 34, No. 3, pp. 320–323, 2011.

    Article  Google Scholar 

  20. Cho, J. S., Kang, S. I., Lee, K. H., Jang, S. H., Kim, I. Y., et al., “Implementation of Gait Analysis System Based on Inertial Sensors,” Journal of Rehabilitation Welfare Engineering & Assistive Technology, Vol. 9, No. 2, pp. 137–144, 2015.

    Google Scholar 

  21. Grood, E. S. and Suntay, W. J., “A Joint Coordinate System for the Clinical Description of Three-Dimensional Motions: Application to the Knee,” Journal of Biomechanical Engineering, Vol. 105, No. 2, pp. 136–144, 1983.

    Article  Google Scholar 

  22. Watanabe, T., Saito, H., Koike, E., and Nitta, K., “A Preliminary Test of Measurement of Joint Angles and Stride Length with Wireless Inertial Sensors for Wearable Gait Evaluation System,” Computational Intelligence and Neuroscience, Vol. 2011, Article No. 6, 2011.

  23. Kim, M.-S., Yu, S.-B., and Lee, K.-S., “Development of a High-Precision Calibration Method for Inertial Measurement Unit,” International Journal of Precision Engineering and Manufacturing, Vol. 15, No. 3, pp. 567–575, 2014.

    Article  Google Scholar 

  24. Perry, J. and Davids, J. R., “Gait Analysis: Normal and Pathological Function,” Journal of Pediatric Orthopaedics, Vol. 12, No. 6, pp. 815, 1992.

    Article  Google Scholar 

  25. Ma, S., Hong, S., Shim, H.-m., Kwon, J.-W., and Lee, S., “A Study on Sitting Posture Recognition Using Machine Learning,” The Transactions of the Korean Institute of Electrical Engineers, Vol. 65, No. 9, pp. 1557–1563, 2016.

    Article  Google Scholar 

  26. Cortes, C. and Vapnik, V., “Support-Vector Networks,” Machine Learning, Vol. 20, No. 3, pp. 273–297, 1995.

    MATH  Google Scholar 

  27. Athisakthi, A. and Pushparani, M., “Detection of Movement Disorders Using Multi SVM,” Global Journal of Computer Science and Technology, Vol. 13, No. 1, Version 1.0, 2013.

    Google Scholar 

  28. Shirakawa, T., Sugiyama, N., Sato, H., Sakurai, K., and Sato, E., “Gait Analysis and Machine Learning Classification on Healthy Subjects in Normal Walking,” International Journal of Parallel, Emergent and Distributed Systems, Vol. 32, No. 2, pp. 185–194, 2017.

    Article  Google Scholar 

  29. Mazilu, S., Hardegger, M., Zhu, Z., Roggen, D., Troster, G., et al., “Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques,” Proc. of 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 123–130, 2012.

    Google Scholar 

  30. Wang, Y. and Makedon, F., “Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes for Cancer Classification Using Microarray Data,” Proc. of Computational Systems Bioinformatics Conference, pp. 497–498, 2004.

    Google Scholar 

  31. Kaur, S. and Kalra, S., “Feature Extraction Techniques Using Support Vector Machines in Disease Prediction,” Proc. of the 4th International Conference on Science, Technology and Management (ICSTM-16), Vol. 5, No. 5, pp. 217–224, 2016.

    Google Scholar 

  32. Kira, K. and Rendell, L. A., “A Practical Approach to Feature Selection,” Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256, 1992.

    Google Scholar 

  33. Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., and Kim, S. I., “Application of Irregular and Unbalanced Data to Predict Diabetic Nephropathy Using Visualization and Feature Selection Methods,” Artificial Intelligence in Medicine, Vol. 42, No. 1, pp. 37–53, 2008.

    Article  Google Scholar 

  34. Karachalios, T., Roidis, N., Papagelopoulos, P. J., and Karachalios, G. G., “The Efficacy of School Screening for Scoliosis,” Orthopedics, Vol. 23, No. 4, pp. 386–391, 2000.

    Google Scholar 

  35. Morrissy, R. T., “School Screening for Scoliosis: A Statement of the Problem,” Spine, Vol. 13, No. 10, pp. 1195–1197, 1988.

    Article  Google Scholar 

  36. Mahaudens, P., Banse, X., Mousny, M., and Detrembleur, C., “Gait in Adolescent Idiopathic Scoliosis: Kinematics and Electromyographic Analysis,” European Spine Journal, Vol. 18, No. 4, pp. 512–521, 2009.

    Article  Google Scholar 

  37. Mahaudens, P., Detrembleur, C., Mousny, M., and Banse, X., “Gait in Adolescent Idiopathic Scoliosis: Energy Cost Analysis,” European Spine Journal, Vol. 18, No. 8, pp. 1160–1168, 2009.

    Article  Google Scholar 

  38. Walter, R. F., Joel A. D., Bruce, M. G., Nicolas, E. W. and Lawrence, R. R., “Scoliosis and Other Spinal Deformities,” Delisa’s Physical Medicine & Rehabilitation, 5th Ed., 2010.

    Google Scholar 

  39. Chan, K., Lee, T.-W., Sample, P. A., Goldbaum, M. H., Weinreb, R. N., and Sejnowski, T. J., “Comparison of Machine Learning and Traditional Classifiers in Glaucoma Diagnosis,” IEEE Transactions on Biomedical Engineering, Vol. 49, No. 9, pp. 963–974, 2002.

    Article  Google Scholar 

  40. Yom-Tov, E. and Inbar, G. F., “Feature Selection for the Classification of Movements from Single Movement-Related Potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 10, No. 3, pp. 170–177, 2002.

    Article  Google Scholar 

  41. Muller, K.-R., Anderson, C. W., and Birch, G. E., “Linear and Nonlinear Methods for Brain-Computer Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, No. 2, pp. 165–169, 2003.

    Article  Google Scholar 

  42. Obermeyer, Z. and Emanuel, E. J., “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine,” The New England Journal of Medicine, Vol. 375, No. 13, pp. 1216–1219, 2016.

    Article  Google Scholar 

  43. Dylan, K., Sean, T. O., Blayne, A. H. and Reed F., “Gait Biomechanics and Patient-Reported Function as Predictors of Response to a Hip Strengthening Exercise Intervention in Patients with Knee Osteoarthritis,” PLOS ONE, Vol. 10, No. 10, Paper No. e0139923, 2015.

    Google Scholar 

Download references

Author information

Author notes

  1. Jae-sung Cho and Young-Shin Cho contributed equally to this work

Authors and Affiliations

  1. Department of Arts and Technology, School of Industrial Information Studies, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

    Jae-sung Cho

  2. Department of Rehabilitation Medicine, Hanyang University College of Medicine, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

    Young-Shin Cho, Sang-Bok Moon, Mi-Jung Kim, Hyeok Dong Lee & Sung Young Lee

  3. Department of Mechatronic Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea

    Young-Hoon Ji

  4. Department of Orthopaedic Surgery, Hanyang University College of Medicine, 153, Gyeongchun-ro, Guri-si, Gyeonggi-do, 11923, Republic of Korea

    Ye-Soo Park

  5. Department of Robot Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea

    Chang-Soo Han

  6. Department of Rehabilitation Medicine, Hanyang University College of Medicine, 153, Gyeongchun-ro, Guri-si, Gyeonggi-do, 11923, Republic of Korea

    Seong-Ho Jang

Authors
  1. Jae-sung Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Young-Shin Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sang-Bok Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mi-Jung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hyeok Dong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sung Young Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Young-Hoon Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ye-Soo Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chang-Soo Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Seong-Ho Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Chang-Soo Han or Seong-Ho Jang.

Additional information

Jae-sung Cho Ph.D. candidate in the Department of Arts and Technology, School of Industrial Information Studies, Hanyang University. His research interest is Biomedical Engineering.

Young-Shin Cho M.D. candidate in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. His research interest is Rehabilitation Medicine.

Sang-Bok Moon Ph.D. candidate in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. His research interest is Rehabilitation Medicine.

Mi-Jung Kim Professor in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. Her research interest is Rehabilitation Medicine.

Hyeok Dong Lee M.D. candidate in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. His research interest is Rehabilitation Medicine.

Sung Young Lee M.D. candidate in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. His research interest is Rehabilitation Medicine.

Young-Hoon Ji Ph.D. candidate in the Department of Mechatronics Engineering, Hanyang University, Seoul, Korea. His research interest is Wearable Robotics.

Ye-Soo Park Professor in the Department of Orthopedic Medicine, Hanyang University College of Medicine. His research interest is Orthopedic Medicine.

Chang-Soo Han Professor in the Department of Robot Engineering, Hanyang University, Ansan, Korea. His research interest is Robotics.

Seong-Ho Jang Professor in the Department of Rehabilitation Medicine, Hanyang University College of Medicine. His research interest is Rehabilitation Medicine.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, Js., Cho, YS., Moon, SB. et al. Scoliosis Screening through a Machine Learning Based Gait Analysis Test. Int. J. Precis. Eng. Manuf. 19, 1861–1872 (2018). https://doi.org/10.1007/s12541-018-0215-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-018-0215-8

Keywords

Search

Navigation