Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke

1. Duncan, PW, Zorowitz, R, Bates, B, et al. Management of adult stroke rehabilitation care: a clinical practice guideline. Stroke 2005; 36: e100–e143.
Google Scholar | Crossref | Medline | ISI2. Collen, FM, Wade, DT, Bradshaw, CM. Mobility after stroke: reliability of measures of impairment and disability. Int Disabil Stud 1990; 12: 6–9.
Google Scholar | Crossref | Medline3. Harris, JE, Eng, JJ, Marigold, DS, et al. Relationship of balance and mobility to fall incidence in people with chronic stroke. Phys Ther 2005; 85: 150–158.
Google Scholar | Crossref | Medline | ISI4. Srikanth, VK, Thrift, AG, Saling, MM, et al. Increased risk of cognitive impairment 3 months after mild to moderate first-ever stroke. Stroke 2003; 34: 1136–1143.
Google Scholar | Crossref | Medline | ISI5. Basilakos, A, Rorden, C, Bonilha, L, et al. Patterns of poststroke brain damage that predict speech production errors in apraxia of speech and aphasia dissociate. Stroke 2015; 46: 1561–1566.
Google Scholar | Crossref | Medline6. King, RB . Quality of life after stroke. Stroke 1996; 27: 1467–1472.
Google Scholar | Crossref | Medline | ISI7. Mukherjee, D., Patil, CG. Epidemiology and the global burden of stroke. World Neurosurg 2011; 76: S85–S90.
Google Scholar | Crossref | Medline | ISI8. Rodgers, MM, Alon, G, Pai, VM, et al. Wearable technologies for active living and rehabilitation: current research challenges and future opportunities. J rehabilitation assistive Tech Eng 2019; 6: 2055668319839607.
Google Scholar | SAGE Journals | ISI9. Patel, S, Hughes, R, Hester, T, et al. A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc IEEE 2010; 98: 450–461.
Google Scholar | Crossref10. Patel, S, Park, H, Bonato, P, et al. A review of wearable sensors and systems with application in rehabilitation. J NeuroEngineering Rehabil 2012; 9: 21.
Google Scholar | Crossref | Medline | ISI11. Sluijs, EM, Kok, GJ, Van der Zee, J. Correlates of exercise compliance in physical therapy. Phys Ther 1993; 73: 771–782.
Google Scholar | Crossref | Medline | ISI12. Kåringen, I, Dysvik, E, Furnes, B. The elderly stroke patient’s long-term adherence to physiotherapy home exercises. Adv Physiother 2011; 13: 145–152.
Google Scholar | Crossref13. Miller, KK, Porter, RE, DeBaun-Sprague, E, et al. Exercise after stroke: patient adherence and beliefs after discharge from rehabilitation. Top Stroke Rehabil 2017; 24: 142–148.
Google Scholar | Crossref | Medline | ISI14. Yao, M, Chen, J, Jing, J, et al. Defining the rehabilitation adherence curve and adherence phases of stroke patients: an observational study. Patient Preference and Adherence 2017; 11: 1435–1441.
Google Scholar | Crossref | Medline15. López-Nava, IH, Muñoz-Meléndez, A. Wearable inertial sensors for human motion analysis: a review. IEEE Sensors J 2016; 16: 7821–7834.
Google Scholar | Crossref16. Taylor, PE, Almeida, GJM, Kanade, T, et al. Classifying human motion quality for knee osteoarthritis using accelerometers. In: International Conference of the Engineering in Medicine and Biology Society, 2010, pp. 339–343.
Google Scholar17. Huang, K, Sparto, PJ, Kiesler, S, et al. A technology probe of wearable in-home computer-assisted physical therapy. In: Proceedings of the 32nd annual ACM Conference on Human Factors in Computing Systems. ACM, 2014, pp. 2541–2550.
Google Scholar | Crossref18. Mannini, A, Trojaniello, D., Cereatti, A, et al. A machine learning framework for Gait classification using inertial sensors: application to elderly, post-stroke and Huntington’s disease patients. Sensors 2016; 16: 134.
Google Scholar | Crossref19. Lang, CE, Waddell, KJ, Klaesner, JW, et al. A method for quantifying upper limb performance in daily life using accelerometers. J Vis Exp Jove.
Google Scholar20. Dobkin, BH, Xu, X, Batalin, M, et al. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke 2011; 42: 2246–2250.
Google Scholar | Crossref | Medline | ISI21. Xu, X, Batalin, MA, Kaiser, WJ, et al. Robust hierarchical system for classification of complex human mobility characteristics in the presence of neurological disorders. In: International Conference on Body Sensor Networks. IEEE, 2011, pp. 65–70.
Google Scholar | Crossref22. Roy, SH, Cheng, MS, Chang, S-S, et al. A combined sEMG and accelerometer system for monitoring functional activity in stroke. IEEE Trans Neural Syst Rehabil Eng 2009; 17: 585–594.
Google Scholar | Crossref | Medline | ISI23. Ferguson, T, Younger, NO, Morgan, ND, et al. Self-reported prevalence of heart attacks and strokes in Jamaica: a cross-sectional study. The Jamaica health and Lifestyle survey 2007–2008. Res Rep Clin Cardiol 2010; 1: 23–31.
Google Scholar24. Bergmann, JHM, McGregor, AH. Body-worn sensor design: what do patients and clinicians want?. Ann Biomed Eng 2011; 39: 2299–2312.
Google Scholar | Crossref | Medline25. Twomey, N, Diethe, T, Fafoutis, X, et al. A comprehensive study of activity recognition using accelerometers. Informatics 2018; 5: 27.
Google Scholar | Crossref26. Gilbert, BK, Haider, CR, Schwab, DJ, et al. A measurement-quality body-worn sensor-agnostic physiological monitor for biomedical applications. Am J Biomed Eng 2015; 5: 34–66.
Google Scholar27. Abadi, M, Barham, P, Chen, J, et al. Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation 2016. Savannah, GA, .
Google Scholar28. Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit-learn: machine learning in python. J Mach Learn Res 2011; 12: 2825–2830.
Google Scholar | ISI29. Ruder, S . An overview of gradient descent optimization algorithms. arXiv Prepr arXiv160904747.
Google Scholar30. Kingma, DP, Ba, J. Adam: a method for stochastic optimization. arXiv Prepr arXiv14126980.
Google Scholar31. Spanier, J, Oldham, KB. The hyperbolic tangent tanh (x) and cotangent coth (x) functions. In: An atlas of functions. Hemisphere, 1987, pp. 279–284.
Google Scholar32. Uswatte, G, Taub, E, Morris, D, et al. The motor activity Log-28: assessing daily use of the hemiparetic arm after stroke. Neurology 2006; 67: 1189–1194, LP – 1194.
Google Scholar | Crossref | Medline | ISI

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