Assisted living infrastructure

• Autorzy: Augustyniak P.
• Języki publikacji: EN

Abstrakty

EN

Assisted living applications are commonly understood as technical environment for disabled or elderly people providing the care in the user-specific range. We are going to present the data capture methodology and design of a home care system for medical-based surveillance and man-machine communication. The proposed system consists of the video-based subject positioning, monitoring of the heart and brain electrical activity and eye tracking. The multimodal data are automatically interpreted and translated to tokens representing subject's status or command. The circadian repetitive status time series (behavioral patterns) are a background for learning of the subject's habits and for automatic detection of unusual behavior or emergency. Due to mutual compatibility of methods and data redundancy, the use of unified status description vouches for high reliability of the recognition despite the use of simplified measurements methods. This surveillance system is designed for everyday use in home care, by disabled or elderly people.

Słowa kluczowe

EN

assisted living; ambient technologies; pervasive computing

PL

wspieranie życia; technologia otoczenia; komputeryzacja

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2011
• Tom: Vol. 17
• Strony: 11--22
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Augustyniak P.

• AGH University of Science and Technology, 30, Mickiewicz Ave., 30-059 Krakow, Poland

Bibliografia

• [1] HRISTOVA A., BERNARDOS AM., CASAR J.R., Context-aware services for ambient assisted living: A casestudy, First International Symposium on Applied Sciences on Biomedical and Communication Technologies, ISABEL '08, 2008, pp. 1-5.
• [2] EKLUND JM., HANSEN TR., SPRINKLE J., SASTRY S., Information Technology for Assisted Living at Home: building a wireless infrastructure for assisted living, 27th Annual International Conference of the IEEE-EMBS, 2005, pp. 3931-3934.
• [3] SUN H., De FLORIO V., GUI N., BLONDIA C., Promises and Challenges of Ambient Assisted Living Systems, Sixth International Conference on Information Technology: New Generations, ITNG '09. 2009, pp. 1201-1207.
• [4] SIXSMITH A., JOHNSON N., A smart sensor to detect the falls of the elderly, IEEE In Pervasive Computing, Vol. 3, No. 2, 2004, pp. 42-47.
• [5] NAIT-CHARIF H., MCKENNA SJ., Activity summarisation and fall detection in a supportive home environment, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. Vol. 4, 2004, pp. 323-326.
• [6] ROUGIER C., MEUNIER J., St-ARNAUD A., ROUSSEAU J., Fall Detection from Human Shape and Motion History Using Video Surveillance, 21st International Conference on Advanced Information Networking and Applications Workshops, AINAW '07, Vol. 2, 2007, pp. 875-880.
• [7] OTTO C., MILENKOVIĆ A., SANDERS C. et al., System architecture of a wireless body area sensor network for ubiquitous health monitoring. Journal of Mobile Multimedia, 2006, Vol. 1, No. 4, pp. 307-326.
• [8] JOVANOV E., MILENKOVIC A., OTTO C., de GROEN P.C., A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation, J. Neuroengineering Rehabil, Vol.2, 2005, pp. 6.
• [9] LIAO W.H., YANG C.M., Video-based Activity and Movement Pattern Analysis in Overnight Sleep Studies, Pattern Recognition, ICPR 2008,
• [10] AUGUSTYNIAK P., TADEUSIEWICZ R., Ubiquitous cardiology: emerging wireless telemedical applications. Hershey, New York: Medical Information Science Reference, 2009.
• [11] NAJAFI B., AMINIAN K., PARASCHIV-IONESCU A., LOEW F., BÜLA ChJ., ROBERT P., Ambulatory System for Human Motion Analysis Using a Kinematic Sensor: Monitoring of Daily Physical Activity in the Elderly, IEEE Transactions on Biomedical Engineering, Vol. 50, No. 6, 2003, pp. 711-723.
• [12] AUGUSTYNIAK P., Compound Personal and Residential Infrastructure for Ubiquitous Health Supervision, in: HIPPE Z.S., KULIKOWSKI JL., MROCZEK T., (eds.), Human-Computer Systems Interaction. Backgrounds and Applications 2, Advances in Soft Computing, Springer Verlag (in print).
• [13] PAN J., TOMPKINS W.J., A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, pp. 32(3):230.
• [14] AUGUSTYNIAK P., Recovering the precise heart rate from sparsely sampled electrocardiograms. Computers in Medicine Conf, 1999, pp. 59.
• [15] CHAZAL P.D., O’DWYER M., REILLY R.B., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng, Vol. 51, 2004, pp. 1196.
• [16] AUGUSTYNIAK P. The use of shape factors for heart beats classification in Holter recordings, Computers in Medicine Conf., 1997, pp. 47.
• [17] SMOLEŃ M., CZOPEK K., AUGUSTYNIAK P., Sleep evaluation device for home-care [in:] PIĘTKA E., KAWA J. (eds.), Information technologies in biomedicine, Vol. 2, Springer-Verlag, (Advances in Intelligent and Soft Computing 69), 2010, pp. 367–378.
• [18] AUGUSTYNIAK P., SMOLEŃ M., BRONIEC A., CHODAK J., Data integration in multimodal home care surveillance and communication system, in: PIĘTKA E, KAWA J (eds.) Information technologies in biomedicine, Vol. 2, Springer-Verlag, 2010, pp 391–402.
• [19] LIU T., INOUE Y., SHIBATA K., Development of a wearable sensor system for quantitative gait analysis, Measurement, doi:10.1016/j.measurement.2009.02.002, 2009.
• [20] ROZENBERG G., Handbook of Graph Grammars and Computing by Graph Transformations, Vol.1 Fundations, World Scientific London, 1997.
• [21] GRABSKA E., ŚLUSARCZYK G., PAPIERNIK K., Interpretation of objects represented by hierarchical graphs, KOSYR’2003, Wroclaw, 2003, pp. 287-293.
• [22] ŚLUSARCZYK G., Hierarchical hypergraph transformations in engineering design, Journal of Applied Computer Science, Vol.11, 2003, pp. 67-82.
• [23] SKOMOROWSKI, M., Syntactic recognition of distorted patterns by means of random graph parsing, Pattern Recognition Letters, 28, 2007, pp. 572-581.
• [24] ŚLUSARCZYK G., AUGUSTYNIAK P. A, graph representation of subject's time-state space [in:] PIĘTKA E., KAWA J. (eds.), Information technologies in biomedicine, Vol. 2, Springer-Verlag, (Advances in Intelligent and Soft Computing 69), pp. 379-390.
• [25] SCHULDT C., LAPTEV I., CAPUTO B., Recognizing human actions: a local SVM approach, Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. Vol. 3, 2004, pp. 32-36.
• [26] AL-ANI T., LE BA Q.T., MONACELLI E., On-line Automatic Detection of Human Activity in Home Using Wavelet and Hidden Markov Models Scilab Toolkits, IEEE 22nd International Symposium on In Intelligent Control, ISIC 2007, 2007, pp. 485-490.
• [27] AUGUSTYNIAK P., Distance Measures in Behavioral Pattern Analysis, accepted for 5 European Medical and Biological Engineering Conference, Budapest, 2011.
• [28] RABINER L., Considerations in dynamic time-warping algorithms for discrete word recognition, IEEE Trans Sig Proc., Vol. 26, 1978, pp. 575-82.
• [29] SYED Z., GUTTAG J., STULTZ C., Clustering and symbolic analysis of cardiovascular signals: discovery and visualization of medically relevant patterns in long-term data with limited prior knowledge, EURASIP Journal on Applied Signal Processing, 2007.
• [30] TADEUSIEWICZ R., OGIELA L., Selected cognitive categorization systems [in:] RUTKOWSKI L., TADEUSIEWICZ R., ZADEH LA., ZURADA JM., (eds.) Artificial Intelligence and Soft Computing, Berlin; Heidelberg: Springer-Verlag, 2008, pp. 1127-1136.