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Human activity detection based on the iBeacon technology

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Paper presents a new method of patient activity monitoring, by using modern ADL (Activities of Daily Living) techniques. Proposed method utilizes energy efficient Bluetooth iBeacon BLE (Bluetooth Low Energy) modules, developed by Apple. Main advantage of this technology is the ability to detect neighboring devices, which belong to the same device family. Proposed method is based on observing changes of received signal strength indicator (RSSI) in the time domain. The RSSI analysis is performed in order to asses a human activity. Such observation may be particularly useful for monitoring consciousness of elder people, where reaction time of emergency rescuers and appropriate rescue operations may save the human lives.
Rocznik
Tom
Strony
38--45
Opis fizyczny
Bibliogr. 13 poz., rys., wykr.
Twórcy
  • University of Silesia in Katowice, Poland
autor
  • University of Silesia in Katowice, Poland
autor
  • University of Silesia in Katowice, Poland
Bibliografia
  • [1] AAMODT K. CC2431 location engine application note AN042, chipcon products for Texas Instruments. 2008.
  • [2] ASHTON K. That internet of things thing. 2009. http://www.rfidjournal.com/articles/view?4986 Online; accessed: 10.07.2016.
  • [3] BORNE K. Big data - what is it good for? 2014. https://mapr.com/blog/big-data-what-it-good/ Online; accessed: 10.07.2016.
  • [4] HARASIMOWICZ A., DZIUBICH T., BRZESKI A. Accelerometer-based human activity recognition and the impact of the sample size. Proceedings of the 13th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, Gdansk, Poland, 2014. pp. 15–17.
  • [5] IBRAHIM H. A., ALY A. K., FAR B. H. A system for vehicle collision and rollover detection. 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016. pp. 1–6.
  • [6] LAU E.-E.-L., CHUNG W.-Y. Enhanced rssi-based real - time user location tracking system for indoor and outdoor environments. Proceedings of the 2007 International Conference on Convergence Information Technology, 2007, ICCIT’07. IEEE Computer Society, pp. 1213–1218.
  • [7] LORENZI P., RAO R., ROMANO G., KITA A., IRRERA F. Mobile devices for the real-time detection of specific human motion disorders. IEEE Sensors Journal, 2016, Vol. 16. pp. 8220–8227.
  • [8] ORCZYK T., PORWIK P., LEWANDOWSKI M., CHOLEWA M. Instance based knn modification for classification of medical data. Journal of MIT, 2015, Vol. 24. pp. 99–106.
  • [9] ÖZDEMIR A. T., ORMAN A. Developing an iphone smartphone based fall detection algorithm. 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015. pp. 2561–2564.
  • [10] PENDLETON H. M. Pedretti’s occupational therapy. practice skills for physical dysfunction. 2013. Elsevier.
  • [11] STATLER S. The hitchhiker’s guide to the beacosystem by stephen statle. 2016. Apress.
  • [12] VATS E., CHAN C. S. Early detection of human actions - a hybrid approach. Applied Soft Computing, 2016, Vol. 46. pp. 953 – 966.
  • [13] YIN J., YANG Q., PAN J. J. Sensor-based abnormal human-activity detection. IEEE Transactions on Knowledge and Data Engineering, 2008, Vol. 20. IEEE, pp. 1082–1090.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3250316b-351f-4076-a96e-7c828eec8804
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