Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden Markov model

2012 ◽  
Vol 6 (6) ◽  
pp. 661-667 ◽  
Author(s):  
V.V.S. Tallapragada ◽  
E.G. Rajan
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Iris Recognition ◽  
Hidden Markov ◽  
Recognition System ◽  
Support Vector

Detection of Irregular Behavior in Room Using Environmental Sensors and Power Consumption of Home Appliances Learning in HMMs

2017 ◽  
Vol 4 (2) ◽  
pp. 94-98
Author(s):  
ShiJie Zhao ◽  
Toshihiko Sasama ◽  
Takao Kawamura ◽  
Kazunori Sugahara
Keyword(s):  
Support Vector Machine ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sensor Network ◽  
Hidden Markov ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Network System ◽  
Daily Lives

We propose a human behavior detect method based on our development system of multifunctional outlet. This is a low-power sensor network system that can recognize human behavior without any wearable devices. In order to detect human regular daily behaviors, we setup various sensors in rooms and use them to record daily lives. In this paper we present a monitoring method of unusual behaviors, and it also can be used for healthcare and so on. We use Hidden Markov Model(HMM), and set two series HMM input to recognize irregular movement from daily lives, One is time sequential sensor data blocks whose sensor values are binarized and splitted by its response. And the other is time sequential labels using Support Vector Machine (SVM). In experiments, our developed sensor network system logged 34days data. HMM learns data of the first 34days that include only usual daily behaviors as training data, and then evaluates the last 8 days that include unusual behaviors. Index Terms—multifunctional outlet system; behavior detection; hidden markov model; sensor network; support vector machine. REFERENCES [1] T.Sasama, S.Iwasaki, and T.Okamoto, “Sensor Data Classification for Indoor Situation Using the Multifunctional Outlet”, The Institute of Electrinical Engineers of Japan, vol.134(7),2014,pp.949-995 [2] M.Anjali Manikannan, R.Jayarajan, “Wireless Sensor Netwrork For Lonely Elderly Perple Wellness”, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, vol. 3, 2015, pp.41-45 [3] Nagender Kumar Suryadevara, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, JUNE 2012, pp. 1965-1972. [4] iTec Co., safety confirmation system: Mimamorou, http://www.minamoro.biz/. [6] Alexander Schliep's group for bioinformatics, The General Hidden Markov Model library(GHMM), http://ghmm.sourceforge.net/. [7] Jr Joe H.Ward, Joumal of the American Statistical Association, vol58(301), 1963, pp236-244 [5] SOLXYZ Co., status monitoring system:Ima-Irumo, http://www.imairumo.com/.

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