Identification of gait patterns related to health problems of elderly

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6406 LNCS, Issue , 2010, Pages 179-191

  Pogorelc, Bogdan ^a,b   Gams, Matjaž ^a,b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b pica International doo   (Slovenia)

Author keywords

ambient intelligence; data mining; gait analysis; Health problems detection; human locomotion; human motion analysis; machine learning

Indexed keywords

AMBIENT INTELLIGENCE; AUTOMATION; DATA MINING; DECISION TREES; GAIT ANALYSIS; HEALTH; LEARNING SYSTEMS; MACHINE LEARNING; UBIQUITOUS COMPUTING; ARTIFICIAL INTELLIGENCE; MEDICAL PROBLEMS;

CLASSIFICATION ACCURACY; GAIT PATTERN; HUMAN LOCOMOTIONS; HUMAN MOTION ANALYSIS; INDEPENDENT LIVING; MOTION CAPTURE SYSTEM; PARKINSON'S DISEASE; POSITION COORDINATES; AUTOMATIC IDENTIFICATION; RANDOM FORESTS;

LEARNING ALGORITHMS;

EID: 85037720524     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16355-5_16     Document Type: Conference Paper

References (25)

1. Toyne, S.: Ageing: Europe's growing problem. BBC News, http://news.bbc.co.uk/2/hi/business/2248531.stm 2009-01-19
2. Strle, D., Kempe, V.: MEMS-based inertial systems. Informacije MIDEM 37(4), 199-209 (2007)
3. Jurman, D., Jankovec, M., Kamnik, R., Topič, M.: Inertial and magnetic sensors: The calibration aspect. Informacije MIDEM 37(2), 67-72 (2007)
4. Dimic, F., Mušič, B., Osredkar, R.: An example of an integrated GPS and DR positioning system designed for archeological prospecting. Informacije MIDEM 38(2), 144-148 (2008)
5. Ribarič, S., Rozman, J.: Sensors for measurement of tremor type joint movements. Informacije MIDEM 37(2), 98-104 (2007)
6. Trontelj, J., Trontelj, J., Trontelj, L.: Safety Margin at mammalian neuromuscular junction - an example of the significance of fine time measurements in neurobiology. Informacije MIDEM 383(3), 155-160 (2008)
7. Bourke, A.K., Lyons, G.M.: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics 30(1), 84-90 (2006)
8. Bourke, A.K., Scanaill, C.N., Culhane, K.M., O'Brien, J.V., Lyons, G.M.: An optimum accelerometer configuration and simple algorithm for accurately detecting falls. In: Proc. BioMed 2006, pp. 156-160 (2006)
9. Confidence: Ubiquitous Care System to Support Independent Living, http://www.confidence-eu.org
10. Craik, R., Oatis, C.: Gait Analysis: Theory and Application. Mosby-Year Book (1995)
11. Harrison, R.D.: Harrison's principles of internal medicine, 14th edn. McGraw Hill, New York (1998)
12. Perry, J.: Gait Analysis: Normal and Pathological Function. McGraw-Hill, Inc., New York (1992)
13. eMotion. Smart motion capture system, http://www.emotion3d.com/smart/ smart.html
14. Kangas, M., Konttila, A., Lindgren, P., Winblad, P., Jamsa, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture 28(2), 285-291 (2008) (Pubitemid 351902256)
15. Lakany, H.: Extracting a diagnostic gait signature. Pattern recognition 41, 1627-1637 (2008)
16. Luštrek, M., Kaluža, B.: Fall Detection and Activity Recognition with Machine Learning. Informatica (Slovenia) 33(2), 197-204 (2009)
17. Maybeck, P.S.: Stochastic models, estimation, and control. Mathematics in Science and Engineering 141 (1979)
18. Qian, G., Guo, F., Ingalls, T., Olson, L., James, J., Rikakis, T.: A gesture-driven multimodal interactive dance system. In: Proc. ICME 2004, pp. 1579-1582 (2004)
19. Sukthankar, G., Sycara, K.: A cost minimization approach to human behavior recognition. In: Proc. AAMAS 2005, pp. 1067-1074 (2005)
20. Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: Proc. ISWC 2007, pp. 37-40 (2007)
21. Vishwakarma, V., Mandal, C., Sura, S.: Automatic detection of human fall in video. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 616-623. Springer, Heidelberg (2007)
22. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
23. Zhang, T., Wang, J., Liu, P., Hou, J.: Fall detection by wearable sensor and One-Class SVM algorithm. LNCIS, vol. 345, pp. 858-863 (1988)
24. Zouba, N., Boulay, B., Bremond, F., Thonnat, M.: Monitoring activities of daily living (ADLs) of elderly based on 3D key human postures. In: Proc. ICVW 2008, pp. 37-50 (2008)
25. Moore, S.T., et al.: Long-term monitoring of gait in Parkinson's disease. Gait Posture (2006) doi:10.1016/j.gaitpost.2006.09.011