Assessing gait patterns of healthy adults climbing stairs employing machine learning techniques

International Journal of Intelligent Systems

Volumn 28, Issue 3, 2013, Pages 257-270

  Chan, Herman ^a   Yang, Mingjing ^a   Wang, Haiying ^a   Zheng, Huiru ^a   McClean, Sally ^a   Sterritt, Roy ^a   Mayagoitia, Ruth E ^b  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION DATA; CLASSIFICATION MODELS; FEATURE SELECTION ALGORITHM; GAIT FEATURES; GAIT PATTERN; HEALTHY SUBJECTS; LEARNING APPROACH; MACHINE LEARNING TECHNIQUES; MULTI LAYER PERCEPTRON; OLDER ADULTS; POOR PERFORMANCE; RANDOM FORESTS; SEQUENTIAL FEATURE SELECTIONS; STAIR-CLIMBING; TRIAXIAL ACCELEROMETER;

ACCELEROMETERS; DECISION TREES; GAIT ANALYSIS; SUPPORT VECTOR MACHINES;

STAIRS;

EID: 84872933264     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.21568     Document Type: Article

References (29)

1. Prince F, Corriveau H, Hébert R, Winter DA,. Gait in the elderly. Gait Posture 1997; 5: 128-135.
2. Barak Y, Wagenaar RC, Holt KG,. Gait characteristics of elderly people with a history of falls: a dynamic approach. Phys Ther 2006; 86: 1501-1510.
3. Kohavi R, John GH,. Wrapper for feature subset selection. Artif Intell 1997; 97 (1/2): 273-324.
4. Vale KO, Neto AF, Canuto AMP,. Using a reinforcement-based feature selection method in classifier ensemble. In: 10th Int Conf on Hybrid Intelligent Systems (HIS); Aug. 23-25, 2010. pp 213-218.
5. Praczyk K, Kiendl H, Slawinski T,. Finding relevant process characteristics with a method for data-based complexity reduction. Lecture Notes Comput Sci 1999; 1625: 548-555.
6. Zheng HR, Yang MJ, Wang HY, McClean S,. Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. In: Intelligent patient management. Berlin, Germany: Springer; 2009. pp 57.
7. Bouten CV, Koekkoek KT, Verduin M, Kodde R, Janssen JD,. A triaxial accelerometry and portable data processing unit for the assessment of daily physical activity. 1997; 44: 136-147.
8. Morris JRW,. Accelerometry-a technique for the measurement of human body movements. J Biomech 1973; 6: 729-736.
9. Godfrey A, Culhane KM, Lyons GM,. Comparison of the performance of the activPAL™ professional physical activity logger to a discrete accelerometer-based activity monitor. Med Eng Phys 2006; 29: 930-934.
10. Sekine M, Tamura T, Akay M, Fujimoto T, Togawa T, Fukui Y,. Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Trans Rehabil Eng 2002; 10: 188-196.
11. Begg R, Palaniswami M,. " Recognition of gait patterns using support vector machines." In:, Begg, R., Palaniswami, M., editors. Computational intelligence for movement sciences: neural networks and other emerging techniques. Hershey, PA: IDEA Group Publishing; 2006.
12. Aminian K, Jéquier R, Schutz Y,. Estimation of speed and incline of walking using neural network. Int J Intell Syst 1995; 44: 743-746.
13. Yang MJ, Zheng HR, Wang HY, McClean S,. Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. In: Proc 3rd Int ICST Conf on Pervasive Computing Technologies for Healthcare. London: April 2009.
14. Startzell JK, Owens A, Mulfinger LM, Cavanagh PR,. Stair negotiation in older people: a review. J Am Geriatr Soc 2000; 48: 567-580.
15. Zachazewski J, Riley P, Krebs D,. Biomedical analysis of body mass transfer during stair ascent and descent of healthy subjects. J Rehabil Res 1993; 30: 412-422.
16. McFadyen BJ, Winter DA,. An integrated biomedical analysis of normal stair ascent and descent. J Biomech 1988; 21: 733-744.
17. Mian OS, Narici MV, Minetti AE, Balzopoulos V,. Centre of mass motion during stair negotiation in young and older men. Gait & Posture 2007; 26: 463-469.
18. Lee H, Chou L,. Balance control during stair negotiation in older adults. J Biomech 2007; 40 (11): 2530-2536.
19. Cleary JG, Trigg LE,. K: an instance-based learner using an entropic distance measure. In: Machine Learning: Proc 12th Int Conf, Tahoe City, CA, 1995. San Francisco, CA: Morgan Kaufmann; 1995, pp 108-114.
20. Vapnik V., The nature of statistical learning theory. Berlin, Germany: Springer-Verlag; 1995
21. Cortes C, Vapnik V,. " Support-vector networks. Mach Learn 1995; 20: 273-297.
22. Bernard I., Multilayer perceptron and uppercase handwritten characters recognition. In: Proc Second Int Conf on Document Analysis and Recognition; 1993. pp 935-938.
23. Irina R,. An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence; 2001.
24. Quinlan JR., C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann; 1993.
25. Breiman L., Random forests. Mach Learn 2001; 45 (1): 5-32.
26. Bouckaert RR, Frank E, Hall MA, Holmes G, Pfahringer B, Reutemann P, Witten Ian H,. WEKA-experiences with a java open-source project. J Mach Learn Res 2010; 11: 2533-2541.
27. Platt J,. Fast training of support vector machines using sequential minimal optimizationm. In:, Schoelkopf B, Burges C, Smola A,., editors. Advances in kernel methods-support vector learning. Cambridge, MA: MIT Press; 1998.
28. Yang M, Zheng H, Wang H, McClean S,. Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. In: PETRA'10, Samos, Greece, June 23-25, 2010.
29. Yu L, Liu H,. Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 2004; 1205-1224.