Browsing by Subject "PIR sensor"

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    Fall detection using single-tree complex wavelet transform
    (Elsevier, 2013) Yazar, A.; Keskin, F.; Töreyin, B. U.; Çetin, A. Enis
    The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.
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    Real-time epileptic seizure detection during sleep using passive infrared sensors
    (Institute of Electrical and Electronics Engineers Inc., 2019) Hanosh, O.; Ansari, R.; Younis, K.; Çetin, A. Enis
    This paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.
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    Resting heart rate estimation using PIR sensors
    (Elsevier B.V., 2017) Kapu, H.; Saraswat, K.; Ozturk, Y.; Çetin, A. Enis
    In this paper, we describe a non-invasive and non-contact system of estimating resting heart rate (RHR) using a pyroelectric infrared (PIR) sensor. This infrared system monitors and records the chest motion of a subject using the analog output signal of the PIR sensor. The analog output signal represents the composite motion due to inhale-exhale process with magnitude much larger than the minute vibrations of heartbeat. Since the acceleration of the heart activity is much faster than breathing the second derivative of the PIR sensor signal monitoring the chest of the subject is used to estimate the resting heart rate. Experimental results indicate that this ambient sensor can measure resting heart rate with a chi-square significance level of α = 0.05 compared to an industry standard PPG sensor. This new system provides a low cost and an effective way to estimate the resting heart rate, which is an important biological marker.
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    Vibrasyon ve PIR algılayıcılar kullanılarak çevre destekli akıllı ev tasarımı
    (IEEE, 2013-04) Yazar, Ahmet; Çetin, A. Enis
    Intelligent ambient assisted living systems for elderly and handicapped people become affordable with the recent advances in computer and sensor technologies. In this paper, fall detection algorithm using multiple passive infrared sensors is developed. As a novel method for detecting a falling person, two passive infrared sensors are used concurrently in a room and developed a determination algorithm depending on the height at which the falling event is happened. Motionles detection system is integrated with the falling person detection system to provide a complete solution. Detection algorithms are implemented using embedded microprocessors. © 2013 IEEE.