Pyroelectric Infrared Sensor Research Articles

Contact-free measurement of respiratory rate using infrared and vibration sensors

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.

A robust system for counting people using an infrared sensor and a camera

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.

Home Appliance Control and Energy Management Using PIR Sensors and ARM Processor

In recent scenario saving energy has become one of the most important issues . The maximum wastage of energy is due the inefficient use of the consumer electronics. These days when there is a demand of comfortable home management but a considerable increase in the electricity bills. The protomodel aims a providing the owner the ability to automatically perform smart load operations providing automatic power cut off system when the home appliances are not in use and also an automatic monitoring of consumption of electricity which alarms the user about his consumption taking into accountuse of home appliances , customer’s preference and customers presence. In the proto model the Pyroelectric infrared (PIR) sensors are used to detect the presence of people in the room and thus perform energy management as they are used as powerful people presence trigerrers. The alarming system is provided by using the smoke and gas sensor and GSM is used in case of emergency. A separate remote control is provided for the flexible control and use of the electric appliances for the comfortable use. A light dependent resistor(LDR) provides th efficient anagement of energy based on residential and outdoor light intensity. The proposed lighting system reduces approximate total power consumption up to25%.And all this is achived by using an ARM processor.

Multimodal Sensor Fusion for Indoor Occupancy Determination

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Indoor localization is used in many applications like security, healthcare, location based services, and social networking. Fingerprinting-based methods are widely used for indoor localization. But received signal strength (RSS) variation due to device diversity and change of conditions in the localization environment (e.g., distribution of furniture, people presence and movement, and opening and closing of doors) induce a significant localization error. To overcome this, we propose a hybrid indoor localization system using radio frequency (RF) and pyroelectric infrared (PIR) sensors. Our localization system has two stages. In the first stage, the zone of the target person is identified by PIR sensors. In the second stage, we apply K-nearest neighbor ( K-NN) algorithm to the fingerprints within the zone identified and estimate position. Zone based processing of fingerprints will exclude deviated fingerprints because of RSS variation. We proposed two localization methods: Proposed_1 and Proposed_2 which use signal strength difference (SSD) and RSS, respectively. Simulation results show that the 0.8-meter accuracy of Proposed_1 achieves 84% and Proposed_2 achieves 65%, while traditional fingerprinting and SSD are 46% and 28%, respectively.

Fusion of Multiple Pyroelectric Characteristics for Human Body Identification

Due to instability and poor identification ability of single pyroelectric infrared (PIR) detector for human target identification, this paper proposes a new approach to fuse the information collected from multiple PIR sensors for human identification. Firstly, Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are adopted to extract features of the human body, which can be achieved by single PIR sensor. Then, we apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to reduce the characteristic dimensions and to classify the human targets, respectively. Finally, Fuzzy Comprehensive Evaluation (FCE) is utilized to fuse recognition results from multiple PIR sensors to finalize human identification. The pyroelectric characteristics under scenarios with different people and/or different paths are analyzed by various experiments, and the recognition results with/without fusion procedure are also shown and compared. The experimental results demonstrate our scheme has improved efficiency for human identification.

Hand gesture based remote control system using infrared sensors and a camera

In this paper, a multimodal hand gesture detection and recognition system using differential Pyroelectric Infrared (PIR) sensors and a regular camera is described. Any movement within the viewing range of the differential PIR sensors are first detected by the sensors and then checked if it is due to a hand gesture or not by video analysis. If the movement is due to a hand, one-dimensional continuous-time signals extracted from the PIR sensors are used to classify/recognize the hand movements in real-time. Classification of different hand gestures by using the differential PIR sensors is carried out by a new winner-takeall (WTA) hash based recognition method. Jaccard distance is used to compare the WTA hash codes extracted from 1-D differential infrared sensor signals. It is experimentally shown that the multimodal system achieves higher recognition rates than the system based on only the on/off decisions of the analog circuitry of the PIR sensors.

Benefits of Wake-Up Radio in Energy-Efficient Multimodal Surveillance Wireless Sensor Network

Scarce energy budget of battery-powered wireless sensor nodes calls for cautious power management not to compromise performance of the system. To reduce both energy consumption and delay in energy-hungry wireless sensor networks for latency-restricted surveillance scenarios, this paper proposes a multimodal two-tier architecture with wake-up radio receivers. In video surveillance applications, using information from distributed low-power pyroelectric infrared (PIR) sensors, which detect human presence limits the activity of cameras and reduces their energy consumption. PIR sensors transmit the information about the event to camera nodes using wake-up radio receivers. We show the benefits of wake-up receivers over duty cycling in terms of overcoming energy consumption versus latency tradeoff (proved with two orders of magnitude lower latency-only 9 ms). At the same time, the power consumption of the camera node, including a wake-up receiver is comparable with the one having only duty-cycled main transceiver with 1% duty cycle (about 32 mW for 25 activations per hour).

Compressive classification of human motion using pyroelectric infrared sensors

Vision-based approaches have been widely applied in motion classification. However, their applicability is often limited by much higher data-loads and computational costs, particularly in the case of constrained recourses. In this paper, a compressive sensing based approach is investigated for motion classification by using pyroelectric infrared (PIR) sensors. We represent a human motion as a spatio-temporal energy sequence (STES) and extract it from an infrared radiation domain. To generate this sequence, a mask is used to divide the object space into small meshes, from which the human-motion induced variances in the infrared radiation will be used to construct a feature. Because of the sparsity of STES, a hardware prototype that is composed of a PIR sensor array and a visibility mask is designed for measuring STES compressively, and a nearest neighbor classifier is then used for classification in the compressive measurement domain. To evaluate the proposed approach, we recorded 360 compressive STESs of ten aerobic exercises performed by six persons. Encouraging experimental results validate the feasibility and efficacy of our approach.

Human movement detection and identification using pyroelectric infrared sensors.

Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and idengification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and idengifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and idengifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules.

Detecting Direction of Movement Using Pyroelectric Infrared Sensors

Pyroelectric infrared (PIR) sensors are widely used as a simple but powerful people presence triggers, e.g., automatic lighting systems. In particular, by alternating the effective polarization of the sensing elements in a PIR sensor, it is possible to determine the relative direction of the movement of an object moving on the motion plane of the PIR sensor. In this paper, we present a novel method of detecting a relative direction of human movement (in eight directions uniformly distributed) with two pairs of PIR sensors whose sensing elements are orthogonally aligned. We have developed a data collection unit with four dual sensing element PIR sensors with modified lenses, and collected data set from six subjects walking in eight directions each. Based on the collected PIR signals, we have performed classification analysis with well known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from two orthogonally aligned PIR sensors with modified lenses, we were able to achieve more than 98% correct detection of direction of movement. We also found that with the reduced feature set composed of three peak values for each PIR sensor, we could achieve 89%-95% recognition accuracy according to machine learning algorithms.

Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network.

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

Design and implementation of a compressive infrared sampling for motion acquisition

This article proposes a compressive infrared sampling method in pursuit of the acquisition and processing of human motion simultaneously. The spatial-temporal changes caused by the movements of the human body are intrinsical clues for determining the semantics of motion, while the movements of short-term changes can be considered as a sparse distribution compared with the sensing region. Several pyroelectric infrared (PIR) sensors with pseudo-random-coded Fresnel lenses are introduced to acquire and compress motion information synchronously. The compressive PIR array has the ability to record the changes in the thermal radiation field caused by movements and encode the motion information into low-dimensional sensory outputs directly. Therefore, the problem of recognizing a high-dimensional image sequence is cast as a low-dimensional sequence recognition process. A database involving various kinds of motion played by several people is built. Hausdorff distance-based template matching is employed for motion recognition. Experimental studies are conducted to validate the proposed method.

A novel low-cost and small-size human tracking system with pyroelectric infrared sensor mesh network

This paper presents a low-cost and small-size human tracking system based on pyroelectric infrared (PIR) sensor mesh network. A wireless pyroelectric sensor network is developed using PIRs and PIR cone optics. The layout of PIRs and sensor nodes are well investigated and the real detection range of PIR is analyzed. A simple and effective PIR signal processing method is designed to get the event signal, and an innovative location method based on detecting angle bisectors of PIRs and data fusion is proposed. An improved Kalman filter and a particle filter are used for human tracking respectively. Simulation and Experimental results have shown that the human tracking system can effectively recognize, locate and track a human target with promising accuracy.

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR) sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The ℓ1 norm minimizations with linear programming (LP) and orthogonal matching pursuit (OMP) are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

Autonomous network repairing of a home security system using modular self-reconfigurable robots

Traditional wheeled robots cannot easily adapt to complex home environments with stairs and doorsills. This paper presents the design and implementation of a novel wireless sensor network based home security system with a modular self-reconfigurable robot (Transmote) as the mobile node. The security system is composed of some static monitoring nodes, a Transmote, and a home server. Static nodes with pyroelectric infrared (PIR) sensor and digital camera are mounted on doorframes and windows to detect intruders and send intrusion alarm messages to the home server. Transmote with PIR sensor and camera can patrol in the house for security surveillance. Transmote also can provide network repairing service when some static router nodes fail. Transmote modules and static monitoring nodes are controlled by two different wireless sensor networks. The remote house owner can get the alarm messages through Internet. Transmote can transform into I-shaped or O-shaped configuration according to different locomotion requirements. A prototype system has been implemented and some performance tests have been done. Experimental results show that the I-shaped Transmote can pass through an 8.8 cm high and 10 cm wide channel. The velocity of the I-shaped Transmote is up to 3 m/min and the maximum velocity of O-shaped Transmote is 9.3 m/min. A 12 kB captured photo can be transmitted to the gateway through 15 hops without any packet loss.

Performance of new generation pole light

This paper describes the design and implementation of a standalone photovoltaic power supply which caters for garden lighting scheme. New Generation Pole Light (NGPL) consists of three parts which are light dependent resistor (LDR) and pyroelectric infrared (PIR) sensors, microcontroller and light emitting diode (LED) and finally, solar charging system. During the night, LED is switched on with two operating modes which are ultra-bright lighting for a predetermine period (when human presence is detected) and dim lighting. Meanwhile, LED is switched off at day time and solar charging system will recover the capacity of discharged battery. NGPL provides portable, sustainable, environmental friendly and requires minimal maintenance for outdoor lighting scheme for both urban and rural areas.

Reduction of False Alarm Signals for PIR Sensor in Realistic Outdoor Surveillance

A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.

Genetic algorithm based optimal placement of PIR sensors for human motion localization

This paper studies the optimal placement of pyroelectric infrared (PIR) sensors in developing the infrared motion sensing system for human motion localization. In particular, we explore the use of genetic algorithm (GA) in optimizing both the deployment and the modulated field of view (FOV) of the PIR sensors for improving the localization performance. Two criteria, the average and maximum localization errors, are used to evaluate the localization performance. In addition, the numerical analysis is presented to offer a guidance on the searching spaces of the design parameters in implementing GA optimization. The proposed GA-based design approach is validated by means of both simulation and experimental studies in the context of human-following mobile robots.

Context-Adaptive Multimodal Wireless Sensor Network for Energy-Efficient Gas Monitoring

We present a wireless sensor network (WSN) for monitoring indoor air quality, which is crucial for people's comfort, health, and safety because they spend a large percentage of time in indoor environments. A major concern in such networks is energy efficiency because gas sensors are power-hungry, and the sensor node must operate unattended for several years on a battery power supply. A system with aggressive energy management at the sensor level, node level, and network level is presented. The node is designed with very low sleep current consumption (only 8 μA), and it contains a metal oxide semiconductor gas sensor and a pyroelectric infrared (PIR) sensor. Furthermore, the network is multimodal; it exploits information from auxiliary sensors, such as PIR sensors about the presence of people and from the neighbor nodes about gas concentration to modify the behavior of the node and the measuring frequency of the gas concentration. In this way, we reduce the nodes' activity and energy requirements, while simultaneously providing a reliable service. To evaluate our approach and the benefits of the context-aware adaptive sampling, we simulate an application scenario which demonstrates a significant lifetime extension (several years) compared to the continuously-driven gas sensor. In March 2012, we deployed the WSN with 36 nodes in a four-story building and by now the performance has confirmed models and expectations.