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The Usefulness of a Wearable Smart Insole for Gait and Balance Analyses After Surgery for Adult Degenerative Scoliosis: Immediate and Delayed Effects

척추측만증 환자의 수술 효과 평가 수단으로서 웨어러블 스마트 깔창을 이용한 보행분석의 유용성

  • Seo, Min Seok (School of Medicine, Pusan National University) ;
  • Shin, Myung Jun (Department of Rehabilitation, Medical Research Institute, Pusan National University Hospital) ;
  • Kwon, Ae Ran (College of Herbal Bio-Industry, Daegu Haany University) ;
  • Park, Tae Sung (Biomedical Research Institude, Pusan National University Hospital) ;
  • Nam, Kyoung Hyup (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital)
  • 서민석 (부산대학교 의학전문대학원) ;
  • 신명준 (부산대학교 의학과 재활의학과학교실) ;
  • 권애란 (대구한의대학교 한방산업학과) ;
  • 박태성 (부산대학교병원 의생명연구원) ;
  • 남경협 (부산대학교 의학과 신경외과학교실)
  • Received : 2020.01.13
  • Accepted : 2020.02.20
  • Published : 2020.02.28

Abstract

This study presents a gait analysis method (including time series analysis) using a smart insole as an objective and quantitative evaluating method after lumbar scoliosis surgery. The participant is a degenerative lumbar scoliosis patient. She took 3-min-gait-test four times(before and 8, 16, and 204-days after surgery) and 6-min-gait-test once(204-days after surgery) with smart-insoles in her shoes. Each insole has 8-pressure sensors, an accelerometer, and a gyroscope. The measured values were used to compare the characteristics of gait before and after surgery. The analysis showed that all of the patient's gait parameters improved after surgery. And after 6 months, the gait was more stable. However, after long walk, the swing duration of one leg was slightly shorter than that of the other again. It was a preclinical problem that could not be found in the visual examination by the practitioner. With this analysis method we could evaluate the improvement of patient quantitatively and objectively. And we could find a preclinical problem. This analysis method will lead to the studies that define and distinguish gait patterns of certain diseases, helping to determine appropriate treatments.

본 연구는 척추측만증 수술에 대한 객관적이고 정량적인 효과 평가 수단으로서 스마트 깔창을 이용한 보행분석 방법(시계열 분석 포함)을 제시한다. 실험 참가자는 척추측만증 환자이며 스마트 깔창을 착용하고 3분 보행검사를 4번(수술전, 수술 후 8일, 16일, 204일), 6분 보행검사를 1번(수술 후 204일) 받았다. 깔창에는 8개의 압력센서, 가속도 및 각속도 센서가 있고, 각각의 측정값을 저장하여 환자의 수술 전후 보행특성(운동역학 및 시공간 변수)을 비교하였다. 분석결과 수술 후 환자의 모든 보행변수가 개선된 것을 알 수 있었고, 6개월 후 추적검사에서 환자의 보행이 더욱 안정된 것을 확인할 수 있었다. 하지만 환자가 오래 걸으면 한쪽 다리의 swing 시간이 다른 쪽에 비해 미세하게 짧은 현상이 다시 나타났는데, 이는 검사를 수행하는 의사의 육안으로는 발견할 수 없는 preclinical한 문제였다. 우리는 이러한 분석 방법을 통해 환자의 개선 정도를 정량적이고 객관적으로 평가할 수 있었고, preclinical한 문제도 찾을 수 있었다. 향후 이러한 분석 방법은 특정 질병의 보행 패턴을 정의하고 감별하여 적절한 치료방법을 결정하는 연구로 이어질 것이다.

Keywords

References

  1. F. P. Hamers, G. C. Koopmans & E. A. Joosten. (2006). CatWalk-assisted gait analysis in the assessment of spinal cord injury. Journal of neurotrauma, 23(3), 537-548. https://doi.org/10.1089/neu.2006.23.537
  2. R. W. Kressig & O. Beauchet. (2006). Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging clinical and experimental research, 18, 174-176. https://doi.org/10.1007/BF03327437
  3. K. Tong & M. H. Granat. (1999). A practical gait analysis system using gyroscopes. Medical engineering & physics, 21(2), 87-94. https://doi.org/10.1016/S1350-4533(99)00030-2
  4. S. J. Bamberg, A. Y. Benbasat, D. M. Scarborough, D. E. Krebs & J. A. Paradiso (2008). Gait analysis using a shoe-integrated wireless sensor system. IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 12(4), 413-423. https://doi.org/10.1109/TITB.2007.899493
  5. D. E. Krebs, J. E. Edelstein & S. Fishman. (1985). Reliability of observational kinematic gait analysis. Physical therapy, 65(7), 1027-1033. https://doi.org/10.1093/ptj/65.7.1027
  6. A. Muro-de-la-Herran, B. Garcia-Zapirain & A. Mendez-Zorrilla. (2014). Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors(Basel, Switzerland), 14(2), 3362-3394. https://doi.org/10.3390/s140203362
  7. P. H. Truong, J. Lee, A. R. Kwon & G. M. Jeong. (2016). Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors. Sensors (Basel, Switzerland), 16(6).
  8. F. Iglesias Vázquez & W. Kastner. (2013). Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns. Energies, 6(2), 579-597. https://doi.org/10.3390/en6020579
  9. Y. Ooi, F. Mita & Y. Satoh. (1990). Myeloscopic study on lumbar spinal canal stenosis with special reference to intermittent claudication. Spine, 15(6), 544-549. https://doi.org/10.1097/00007632-199006000-00021
  10. C. Frigo, M. Rabuffetti, D. C. Kerrigan, L. C. Deming & A. Pedotti. (1998). Functionally oriented and clinically feasible quantitative gait analysis method. Medical & biological engineering & computing, 36(2), 179-185. https://doi.org/10.1007/BF02510740