Browsing by Author "Alkar, Ali Ziya"

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Citation Count: 20
Contact-Free Measurement of Respiratory Rate Using Infrared and Vibration Sensors
(Elsevier Science Bv, 2015) Erden, Fatih; Erden, Fatih; Alkar, Ali Ziya; Cetin, Ahmet Enis; Department of Electrical & Electronics Engineering
Respiratory rate is an essential parameter in many practical applications such as apnea detection, patient monitoring, and elderly people monitoring. In this paper, we describe a novel method and a contact-free multi-modal system which is capable of detecting human breathing activity. The multimodal system, which uses both differential pyro-electric infrared (PIR) and vibration sensors, can also estimate the respiratory rate. Vibration sensors pick up small vibrations due to the breathing activity. Similarly, PIR sensors pick up the thoracic movements. Sensor signals are sampled using a microprocessor board and analyzed on a laptop computer. Sensor signals are processed using wavelet analysis and empirical mode decomposition (EMD). Since breathing is almost periodic, a new multi-modal average magnitude difference function (AMDF) is used to detect the periodicity and the period in the processed signals. By fusing the data of two different types of sensors we achieve a more robust and reliable contact-free human breathing activity detection system compared to systems using only one specific type of sensors. (C) 2015 Elsevier B.V. All rights reserved.
Citation Count: 9
A Robust System for Counting People Using an Infrared Sensor and a Camera
(Elsevier, 2015) Erden, Fatih; Erden, Fatih; Alkar, Ali Ziya; Cetin, Ahmet Enis; Department of Electrical & Electronics Engineering
In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner. (c) 2015 Elsevier B.V. All rights reserved.
Citation Count: 78
Sensors in Assisted Living a Survey of Signal and Image Processing Methods
(Ieee-inst Electrical Electronics Engineers inc, 2016) Erden, Fatih; Erden, Fatih; Velipasalar, Senem; Alkar, Ali Ziya; Cetin, A. Enis; Department of Electrical & Electronics Engineering
[No Abstract Available]