Detection of human fall episodes based on coordinates of body tags and numerical differentiation

• Autorzy: Momot M. , Momot A. , Nowak G. , Seredynski R. , Jezewski J. , Kupka T.
• Języki publikacji: EN

Abstrakty

EN

This paper presents a new method for detection of changes in alignment of the human body, particularly the fall, on the basis of signals acquired from the position sensors placed on the body of the monitored person. The sensors are located on the cuffs, waist and chest. Transformation of data sequence collected from sensors is proposed in order to best distinguish between the collapse from the normal movement. It is based on nonlinear combination of the first two derivatives of the signals being read. Because data from the sensors is sent asynchronously, a numerical algorithm for unevenly sampled data differentiation is proposed. Derivative values are calculated in equidistant nodes through differentiation of a polynomial, which is adjusted by minimizing the mean square error. The developed method can be used in home care telemedicine systems, where it is necessary to long term monitor of multiple vital parameters of people under care.

Słowa kluczowe

EN

ambient; assisted living; fall detection; irregular sampled data; numerical differentiation

PL

otoczenie; wspieranie życia; dane nieregularne objęte próbą; zróżniczkowanie numeryczne

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 21
• Strony: 11--17
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Momot M.

• Institute of Medical Technology and Equipment ITAM, Roosevelt 118 Street, 41-800 Zabrze

autor

Momot A.

autor

Nowak G.

autor

Seredynski R.

autor

Jezewski J.

autor

Kupka T.

Bibliografia

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• [2] CISMONDI F., FIALHO A.S., VIEIRA S.M. et al., Computational intelligence methods for processing misaligned, unevenly sampled time series containing missing data. Proc. IEEE Symp. on Computational Intelligence and Data Mining, Paris, 2011, pp. 224–231.
• [3] JEZEWSKI J., WROBEL J., HOROBA K., et. al., Computerized perinatal database for retrospective qualitative assessment of cardiotocographic traces, in: Current Perspectives in Healthcare Computing, Editor: B. Richards, BJHC Limited, Great Britain, 1996, pp. 187-196.
• [4] KALUZA B., MIRCHEVSKA V., DOVGAN E. et al., An agent-based approach to care in independent living. Proc. 1st Int. Joint Conf. on Ambient intelligence, Malaga, 2010, p.177-186.
• [5] LIAO Y.T., HUANG C.-L., HSU S.-C., Slip and fall event detection using Bayesian Belief Network. Pattern Recognition, 2012, Vol. 45, No. 1, pp. 24–32.
• [6] OLIVIERI D.N., CONDE I.G., SOBRINO X.A.V., Eigenspace-based fall detection and activity recognition from motion templates and machine learning. Expert Systems with Applications, 2012, Vol. 39, No. 5, pp. 5935–5945.
• [7] PRZYBYLA T., JEZEWSKI J., WROBEL J., Robust Fuzzy Clustering using Adaptive Fuzzy Meridians, in: Intelligent Information and Database Systems, Editors: N.T. Nguyen, M.T. Le, J. Swiatek, LNAI 5990/Lecture Notes in Computer Science, Springer Verlag, 2010, pp. 200-209.
• [8] WANG L., GU T., Tao X. et al., A hierarchical approach to real-time activity recognition in body sensor networks. Pervasive and Mobile Computing, 2012, Vol. 8, No. 1, pp. 115–130.