Passive infrared (PIR) sensors are the most popular deployed sensors in buildings for individual presence detection. However, PIR sensors are motion detectors in nature, responding only to incident radiation variation, which leads to false negative detections, inaccurate occupancy estimation, and uncomfortable lighting swings and waste of energy. To address this issue, an optical shutter driven by a Lavet motor PIR (LAMPIR) sensor is developed for true presence detection. In comparison with our previously developed chopped PIR (C-PIR) and rotationally-chopped PIR (Ro-PIR) sensors, a low-power single-phase electro-mechanical driving approach, is introduced for LAMPIR, to replace traditional servo and stepper motors, and thus significantly reduce the power consumption by up to 89%, size by up to 60%, weight by up to 75%, cost by up to 31%, and acoustic noise by 12 dBA. More specifically, driven by pulsed signal from a microcontroller unit (MCU), the electro-mechanical vibrator drives a semi-transparent long-wave infrared (LWIR) optical shutter to chop the field of view (FOV) of a PIR sensor periodically. By monitoring and analyzing the voltage outputs generated by the LAMPIR senor, high-accuracy presence detection can be achieved, by optimizing the shutter width and shuttering period through parametric studies. Experimental results show that a classification accuracy of 100% can be reached for detecting stationary occupants up to 4.5 m and moving occupants up to 10 m, suggesting a detection range improvement from both the C-PIR and the Ro-PIR sensors (4.0 m for stationary and 8.0 m for moving occupancy detection for both sensors). Additionally, the LAMPIR sensor has a FOV of 90° in horizontal and 100° in vertical, which is sufficient for most applications. For a 31-hour-long presence detection test, an accuracy of 97% is achieved when classifying unoccupied and occupied scenarios, while an accuracy of 93% when classifying unoccupied, stationary and moving occupant scenarios.

Passive infrared (PIR) sensors are the current choice for individual presence detection in buildings. A major problem is that PIR sensors only detect moving individuals, which often provides false negative detections, resulting in uncomfortable lighting/temperature swings, short lifetime of the equipment, and waste of energy. In this letter, a rotating shutter is introduced to the rotationally-shuttered PIR (Ro-PIR) sensor to explore its functionalities beyond motion sensing, including stationary individual localization, tracking, and facing direction detection. More specifically, by monitoring and analyzing polarity-phase of the nonlinear infrared energy flow induced by the rotating shutter, Ro-PIR is capable of zone-level localization for stationary individuals. By further analyzing the duty cycle of the output, occupancy facing direction can be predicted. Two theoretical models are created to identify facing directions (front/back or side) by analyzing three infrared radiation covering configurations, shaped by shutter movement. Combining sequence of covering configurations represents the induced polarity-phase output voltage, illustrating the occupancy localization information during one segmented scanning period. Parametric analysis, and empirical studies are performed to obtain the optimal setting of the shutter in terms of its width and shuttering period. Experimental results reveal 100% presence accuracy for stationary detection for up to 3.0 m, and moving detection for up to 8.0 m. Zone-level localization can reach 98% accuracy by applying machine learning classifier using two features extracted from polarity-phase signals. Experimental results also show less than 0.44 m root mean square error (RSME) for tracking, and over 83% in detecting front/back or side facing direction.

Passive infrared (PIR) sensors, currently used for indoor lighting control report frequent false negative detects for stationary occupancy, which takes up almost 50% of the total occupancy rate. To address this, we developed a synchronized low-energy electronically chopped PIR (SLEEPIR) sensor that incorporates a liquid crystal (LC) shutter to chop the long-wave infrared signal received by the on-board PIR sensor. In this paper, we present a SLEEPIR sensor module, integrated with a PIR and machine learning for systematic evaluation of true occupancy detection in daily life. We design complex experimental scenarios containing a series of continuous daily activities and individual actions to simulate realistic environment to the maximum extend. We extract and down select key statistical features using recursive feature elimination with cross validation. In the end, we compared six machine learning models to evaluate the detection performance. Experiments involving continuous daily activities indicate an accuracy of 99.12% by using the support vector machine classifier.

We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor that can detect both stationary and moving occupants by incorporating a liquid crystal (LC) shutter with a traditional passive infrared (PIR) sensor. However, its detection accuracy is still largely impacted by environmental infrared noises. In this paper, we present a Particle Filter (PF) based system-level algorithm that employs a network of SLEEPIR sensors which are installed at different points of interest within an indoor space. The method interprets the incoming observations from the field of view (FOV) of each sensor via the likelihood function to update the state of the PF. The PF output is a probability density function (pdf) that represents the occupancy state of the entire observed space. The sensor location, observation cone, range, observation frequency and historic inter-sensor correlation are the key parameters that contribute to the likelihood function design. Since the method utilizes the historic correlation among sensors, the pairs of correlating sensors often perform self-correction whenever a faulty observation is encountered due to either sensor limitations or due to environmental noise. Occupancy is established through a thresholding function applied to the output pdf of the PF. A lab-based dataset was collected over a period of 360 hours using the SLEEPIR sensor system. Results indicate an average 8.25% occupancy accuracy improvement when compared to the accuracy state delivered by individual SLEEPIR nodes.

Compressive sensing based indoor human detection has raised accumulative attention because it could recover sparse signals from fewer samples than that needed by the Nyquist sampling theorem. However, their application is limited due to high computation cost and large deployment effort. To address this, this letter proposes the use of a rotating binary mask and a single thermopile point detector to enable compressive sensing for indoor human positioning. Traditionally, a thermopile point detector could only convert the received infrared radiation into electrical signals. By using a periodically rotated coded binary mask, the infrared radiation received by the detector will be sampled compressively, and a compressed signal sequence will be obtained after a few times of measurement. The mask consists of 8 sub-masks and 7 of them are designed following the pattern of a randomly generated binary matrix. A recovery algorithm is implemented to recover the original spatial distribution of the infrared radiation within the field of view. Experimental results demonstrate the zone-level positioning with high accuracy. The proposed sensor is compact, mobile, cheap, easy to deploy and therefore has great potential for occupancy state monitoring, such as occupant centered thermal comfort control.

By using a liquid crystal shutter to periodically modulate the infrared energy received by the on-board passive infrared (PIR) sensor, the synchronized low energy electronically chopped PIR sensor recently developed by our team can detect stationary occupancy. However, the fixed threshold value-based detection accuracy is largely dependent on environmental settings, such as the room and floor surface temperature. In this letter, we first developed a thermoelectric model to quantify the environmental impact on the threshold value and then created an adaptive algorithm to generate temperature-sensitive threshold values to compensate environmental effects. We validated our thermoelectric model and tested our adaptive algorithm in two uncontrolled environmental settings, and results show that the detection accuracy reaches over 92%, which is much higher than that using the fixed threshold approach.