Objectively quantifying walking ability in degenerative spinal disorder patients using sensor equipped smart shoes

Medical Engineering and Physics

Volumn 38, Issue 5, 2016, Pages 442-449

  Lee, Sunghoon Ivan ^a,b,c,d   Park, Eunjeong ^c,d   Huang, Alex ^a   Mortazavi, Bobak ^c,d,f   Hayward Garst, Jordan ^e   Jahanforouz, Nima ^e   Espinal, Marie ^e   Siero, Tiffany ^e   Pollack, Sophie ^e   Afridi, Marwa ^e   Daneshvar, Meelod ^e   Ghias, Saif ^e   Lu, Daniel ^d,e   Sarrafzadeh, Majid ^c,d  

^a HARVARD MEDICAL SCHOOL   (United States)

^b American Journal of Physical Medicine and Rehabilitation   (United States)

^c Department of Civil and Environmental Engineering   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Functional level; Lumbar spinal stenosis; Pressure mapping; Self paced walking test; Smart shoes; Spinal cord disorder; Walking ability

Indexed keywords

ARTIFICIAL INTELLIGENCE; CORRELATION METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS;

FUNCTIONAL LEVELS; PRESSURE MAPPING; SMART SHOES; SPINAL CORDS; SPINAL STENOSIS; WALKING ABILITY;

PATIENT TREATMENT;

ADULT; AGED; ARTICLE; AUTOMATION; BODY HEIGHT; BODY MASS; BODY WEIGHT; CLINICAL ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; DAILY LIFE ACTIVITY; DECOMPRESSION; FEMALE; FOLLOW UP; FUNCTIONAL ASSESSMENT; FUNCTIONAL STATUS; HUMAN; LEARNING ALGORITHM; LUMBAR SPINAL STENOSIS; MACHINE LEARNING; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; ORTHOPEDIC SHOE; OUTCOME ASSESSMENT; PRIORITY JOURNAL; SENSOR EQUIPPED SMART SHOE; SMOKING; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; WALKING; DEVICES; GAIT; LUMBAR VERTEBRA; MIDDLE AGED; PATHOPHYSIOLOGY; PHYSIOLOGIC MONITORING; POSTOPERATIVE PERIOD; PREOPERATIVE PERIOD; PRESSURE; SHOE; SPATIOTEMPORAL ANALYSIS; SPINAL STENOSIS;

ADULT; AGED; FEMALE; GAIT; HUMANS; LUMBAR VERTEBRAE; MACHINE LEARNING; MALE; MIDDLE AGED; MONITORING, PHYSIOLOGIC; POSTOPERATIVE PERIOD; PREOPERATIVE PERIOD; PRESSURE; SHOES; SPATIO-TEMPORAL ANALYSIS; SPINAL STENOSIS; WALKING;

EID: 84959894932     PISSN: 13504533     EISSN: 18734030     Source Type: Journal    
DOI: 10.1016/j.medengphy.2016.02.004     Document Type: Article

References (31)

1. Katz J.N., Harris M.B. Lumbar spinal stenosis. New Engl J Med 2008, 358(8):818-825.
2. Stucki G., Daltroy L., Liang M.H., Lipson S.J., Fossel A.H., Katz J.N. Measurement properties of a self-administered outcome measure in lumbar spinal stenosis. Spine 1996, 21(7):796-803.
3. Rainville J., Childs L.A., Pea E.B., Suri P., Limke J.C., Jouve C., et al. Quantification of walking ability in subjects with neurogenic claudication from lumbar spinal stenosis a comparative study. Spine J 2012, 12(2):101-109.
4. Jackson R.P., McManus A.C., Moore J. Lumbar spinal stenosis: treatment options for an aging population. Mo Med 2012, 109(6):466-469.
5. Porter R.W. Spinal stenosis and neurogenic claudication. Spine 1996, 21(17):2046-2052.
6. Conway J., Tomkins C.C., Haig A.J. Walking assessment in people with lumbar spinal stenosis: capacity, performance, and self-report measures. Spine J 2011, 11(9):816-823.
7. Tomkins-Lane C.C., Conway J., Hepler C., Haig A.J. Changes in objectively measured physical activity (performance) after epidural steroid injection for lumbar spinal stenosis. Arch Phys Med Rehabil 2012, 93(11):2008-2014.
8. Deyo R., Mirza S.K., Martin B.I., Kreuter W., Goodman D.C., Jarvik J.G. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA 2010, 303(13):1259-1265.
9. Tuli S.K., Yerby S.A., Katz J.N. Methodological approaches to developing criteria for improvement in lumbar spinal stenosis surgery. Spine 2006, 31(11):1276-1280.
10. Pratt R.K., Fairbank J.C., Virr A. The reliability of the shuttle walking test, the Swiss spinal stenosis questionnaire, the Oxford spinal stenosis score, and the Oswestry Disability Index in the assessment of patients with lumbar spinal stenosis. Spine 2002, 27:84-91.
11. Tomkins-Lane C.C., BattiÈr M.C., Macedo L.G. Longitudinal construct validity and responsiveness of measures of walking capacity in individuals with lumbar spinal stenosis. Spine J 2014, 14(9):1936-1943.
12. Fairbank J., Pynsent P.B. The Oswestry Disability Index. Spine 2000, 25(22):2940-2953.
13. Kopec J.A. Measuring functional outcomes in persons with back pain: a review of back-specific questionnaires. Spine 2000, 15:3110-3114.
14. Institute of Electrical and Electronics Engineers Inc. IEEE Std. 802.15.4-2003 wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (LR-WPANs) 2003, IEEE Press.
15. Perry J. Gait analysis: normal and pathological function 2010, Slack, Thorofare, NJ.
16. Finkelstein J.A. Response shift following surgery of the lumbar spine 2010, Master's thesis; University of Toronto; Toronto, Canada.
17. Knutsson B., Sandn B., Sjdn G., Jrvholm B., Michalsson K. Body mass index and risk for clinical lumbar spinal stenosis: A cohort study. Spine September 2015, 40(18):1451-1456.
18. Sobottke R., Herren C., Siewe J., Mannion A., Rder C., Aghayev E. Predictors of improvement in quality of life and pain relief in lumbar spinal stenosis relative to patient age: a study based on the spine tango registry. Eur Spine J 2015, 1-11.
19. Sigmundsson F.G., Kang X.P., Jöunsson B., Strmqvist B. Prognostic factors in lumbar spinal stenosis surgery. Acta Orthopaedica 2012, 83(5):536-542.
20. Sandn B., Forsth P., Michaelsson K. Smokers show less improvement than nonsmokers two years after surgery for lumbar spinal stenosis: A study of 4555 patients from the swedish spine register. Spine June 2011, 36(13):1059-1064.
21. Robinson R.O., Herzog W., Nigg B.M. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J Manip Physiol Therapeutics 1987, 10(1):172-176.
22. Keerthi S., Shevade S., Bhattacharyya C., Murthy K. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput 2001, 13(3):637-649.
23. Ustun B., Melssen W., Buydens L. Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemom Intell Lab Syst 2006, 81(1):29-40.
24. Shevade S., Keerthi S., Bhattacharyya C., Murthy K. Improvements to the SMO algorithm for SVM regression. Neural Netw IEEE Trans 2000, 11(5):1188-1193.
25. Robnik-ikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 2003, 53(1-2):23-69.
26. Bezdek J., Pal N. Some new indexes of cluster validity. Syst Man Cybernet Part B Cybernet IEEE Trans 1998, 28(3):301-315.
27. Data Mining: Practical Machine Learning Tools and Techniques 2011, Morgan Kaufmann, Boston. 3rd. I.H. Witten, E. Frank, M.A. Hall (Eds.).
28. Davies D.L., Bouldin D.W. A cluster separation measure. IEEE Trans Patt Anal Mach Intell 1979, PAMI-1(2):224-227.
29. Hall M.A. Correlation-based feature subset selection for machine learning 1998, Ph.D. thesis; University of Waikato.
30. Chang C.-C., Lin C.-J. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:1-27.
31. Tomkins C.C., Battie M.C., Rogers T., Jiang H., Petersen S. A criterion measure of walking capacity in lumbar spinal stenosis and its comparison with a treadmill protocol. Spine 2009, 34(22):2444-2449.