Stationary Occupants Research Articles

Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor node to detect stationary and moving occupants. It uses a liquid crystal shutter to modulate the infrared signal received by a traditional passive infrared (PIR) sensor and thus enables its capability to detect stationary occupants. However, the detection accuracy of the SLEEPIR sensor can be easily influenced by infrared environmental disturbances. To address this problem, in this paper, we propose two Long Short-Term Memory (LSTM) models to filter infrared environmental disturbance, named baseline LSTM (Base.LSTM) and statistical LSTM (Stat.LSTM). They use the sensor node raw output and statistical features as their respective input. For comparison, we propose two other models: the occupancy state switch detection (SSD) algorithm that directly uses a predetermined threshold voltage value to classify the occupancy state and its status change; and the multi-layer perception (MLP) classifier with statistical feature inputs (Stat.ML). To validate their detection performance, we designed two testing scenarios in different environment settings: (i) Daily occupancy tests and (ii) EDGE case tests. The first scenario intends to restore complex real-life environmental situations as much as possible in the lab and apartment rooms. The second scenario aims to verify their detection accuracy under different environmental temperatures. This scenario also considers different occupancy postures, such as lying down. Experimental results show that the detection accuracy of both LSTM models (> 95%) in both testing scenarios outperforms that of the SSD (around 82% to 94%) and the Stat.ML(around 80% to 90%).

Performance Optimization of the SLEEPIR Sensor Towards Indoor Stationary Occupancy Detection

To enable passive infrared (PIR) sensors to detect stationary occupants, we recently invented a synchronized low energy electronically chopped PIR (SLEEPIR) sensor consisting of PIR sensor, a polymer dispersed liquid crystal (PDLC) infrared shutter and a driving circuit. By electrically chopping the long-wave infrared (8- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12~\mu \text{m}$ </tex-math></inline-formula> ) radiation received by the PIR sensor, the SLEEPIR sensor can detection both stationary and moving occupants. In this paper, we developed a theoretic model to better understand the output of the SLEEPIR) sensor, and thus optimize its fabrication process and detection performance. The model indicated that its performance is mainly depending on the effective modulation and response time of the PDLC shutter in addition to the time constants of the PIR sensor. We empirically found that the effective modulation and response time are largely dependent on the frequency and amplitude of the driving signal, as well as the mass ratio, cell gap and cooling rate of the PDLC shutter. In the end we obtained optimized parameters that can produce the maximal SLEEPIR output, and experimentally validated our obtained optimal parameters and fabrication conditions.

SLEEPIR: Synchronized Low-Energy Electronically Chopped PIR Sensor for True Presence Detection

Passive infrared (PIR) sensors are widely used in motion sensing and lighting control. However, stationary occupants always trigger false-negative detects of PIR sensors and result in uncomfortable lighting swings and short lifetime of electronic appliances. To address this issue, a synchronized low-energy electronically chopped PIR (SLEEPIR) sensor is developed in this letter for true presence detection, where a liquid crystal (LC) optical shutter is proposed to electrically chop signals received by the PIR sensor in the long-wave infrared (LWIR) region (8–12 μ m), where human skin radiates the most. Utilizing the anisotropic absorption property of the LC material, the shutter shows high modulation with a low driving voltage ( ${V_{{\rm{rms}}}}$ = 4.17 V). Instead of using the mechanical shutters previously developed by our group that are driven by motors, the SLEEPIR sensor reduces the power consumption,weight, volume, and noise level by 90.5%, 83.8%, 82.6%, and 100%, respectively, while maintaining high sensitivity and reliability. Thanks to the high modulation of the LC shutter in the LWIR region, the experimental results show that the SLEEPIR sensor could detect stationary occupants with high accuracy within the range of 4.0 m and with a large field-of-view of 100°.

Human-induced vibration of cold-formed steel floor systems: Parametric studies

Designing a lightweight floor to prevent annoying vibrations induced by human activities is still a challenge because numerous factors need to be considered. In consequence, there is still a lack of reliable models and adequate design guidelines pertinent to the vibration serviceability of cold-formed steel floor systems. To facilitate understanding the fundamental concepts of lightweight floor vibrations for serviceability design, a newly proposed damped plate-oscillator model was adopted in this research to predict dynamic responses of cold-formed steel floors induced by human walking. Three loading methods were developed based on this model. By using these loading methods, comprehensive parametric studies involving step frequencies, mass ratios, damping ratios, walking paths and end-support restraints were conducted. The present analytical studies show that the influence of moving or stationary occupants depends on the mass ratio of occupants to the floor and its significance could be negligible for small mass ratios. In addition, the boundary restraints at floor end-supports may not always reduce floor responses. On the contrary, relatively larger responses could be excited by human walking for the restrained floors having the fundamental frequency close to the multiple of the footstep frequency.

Indoor Occupancy Awareness and Localization Using Passive Electric Field Sensing

This paper introduces sparse low-power sensor networks using passive electric potential sensors for crowd-aware smart buildings. The proposed networks can detect human presence, track their positions, and estimate room occupancy by measuring the electric field disturbances caused by human movement. In addition, the proposed passive sensing system can remotely monitor human physiological signals of nearby stationary occupants (standoff ~1.5 m) when the ambient 60-Hz fields are filtered out. Based on an experimentally calibrated source range model, we further propose an improved multiple-occupant near-field indoor localization method using a maximum likelihood algorithm combined with dividing rectangular search. The tradeoff between search efficiency and localization accuracy is also discussed. Experimental results in two different rooms for 1 week duration show the effectiveness of our proposed sensing system: human identification with an accuracy of 98.3% on six participants, occupant localization methods with room occupancy estimation accuracy of 89.03%, and average localization error less than 0.3 m. These results open up new possibilities in security, indoor navigation or positioning systems, human–machine interfaces, and tele-health applications.

A Low-Power Electric-Mechanical Driving Approach for True Occupancy Detection Using a Shuttered Passive Infrared Sensor

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, the electro-mechanical vibrator drives a semi-transparent long-wave infrared 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 an FOV of 90° in horizontal and 100° in vertical, which is sufficient for most applications. For a 31-h-long presence detection test, an accuracy of 97% is achieved when classifying unoccupied and occupied scenarios, while an accuracy of 93% is achieved when classifying unoccupied, stationary and moving occupant scenarios.

Turning a pyroelectric infrared motion sensor into a high-accuracy presence detector by using a narrow semi-transparent chopper

Pyroelectric infrared (PIR) sensors are the most popular presence detectors. However, their applications are limited to motion detection only, as the pyroelectric sensing element is only sensitive to radiated heat power fluctuation. This letter reports a chopped PIR (C-PIR) presence sensor, capable of detecting both the stationary and the moving occupants at high accuracy by introducing a narrow semi-transparent optical chopper to shutter the incident radiation periodically. More importantly, even though the narrow chopper can efficiently shutter the unidirectional infrared (IR) power radiated by stationary occupants, it has near-zero influence on the omnidirectional power radiated from the ambient environment. Therefore, the voltage difference generated by the C-PIR sensor between occupied and unoccupied scenarios can be directly used for identifying the presence of stationary occupants with high-accuracy. For moving occupant detection, the optical chopper can barely affect the variation of the radiated power and thus lead to the same high-accuracy detection as the on-board PIR. An approximate theoretical model is created to illustrate the physics and dynamics of IR radiation shuttered by the optical chopper. Parametric studies, experimental characterization, and empirical analysis were performed to obtain the optimal setting of the chopper in terms of its thickness and chopping frequency. Experimental results reveal that the C-PIR sensor can detect stationary occupants for up to 4.0 m and moving occupants for up to 8.0 m, while maintaining an accuracy of 100%. The field of view of the C-PIR was found at 110° horizontally and 90° vertically, consistent with that of the on-board PIR sensor.

Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations

Abstract Building energy management and the necessity to reduce overall energy consumption is becoming an increasingly important topic. Especially in dynamic environments, where the setting and occupancy keep changing, knowing occupancy information, including the number and identities of the occupants and where they are located, can be beneficial in energy management as well as other application areas including safety, security and emergency response. In particular, occupancy information has a direct impact on various aspects of heating, ventilation, and air conditioning (HVAC) systems, such as heat loads, system running time, required heating, cooling and distribution of conditioned air, and preferred temperature set points. Energy-saving strategies can be carried out in response to real-time occupancy changes. In this paper, an RFID based occupancy detection system is proposed to support demand-driven HVAC operations by detecting and tracking multiple stationary and mobile occupants in multiple spaces simultaneously. The proposed system estimates the thermal zone where each occupant is located, and reports the number of occupants for each thermal zone in real time. The field tests yielded an average zone level detection accuracy of 88% for stationary occupants and 62% for mobile occupants. For scattering analysis, averages distances to corresponding centroids were 1.45 m and 3.24 m for stationary and mobile occupants, respectively. In order to explore the benefits of demand-driven HVAC operations, current HVAC work procedures are examined, major energy consumers in HVAC systems are identified and quantified, and energy-saving strategies are presented. This study aims to support reducing the consumption of the HVAC systems by integration of the occupancy detection system and the demand-driven HVAC operation strategies.

Thoracic Injury Metrics With Side Air Bag: Stationary and Dynamic Occupants

Objective: Injury risk from side air bag deployment has been assessed using stationary out-of-position occupant test protocols. However, stationary conditions may not always represent real-world environments. Therefore, the objective of the present study was to evaluate the effects of torso side air bag deployment on close-proximity occupants, comparing a stationary test protocol with dynamic sled conditions. Methods: Chest compression and viscous metrics were quantified from sled tests utilizing postmortem human specimens (PMHS) and computational simulations with 3 boundary conditions: rigid wall, ideal air bag interaction, and close-proximity air bag deployment. PMHS metrics were quantified from chestband contour reconstructions. The parametric effect of ΔV on close-proximity occupants was examined with the computational model. Results: PMHS injuries suggested that close-proximity occupants may sustain visceral trauma, which was not observed in occupants subjected to rigid wall or ideal air bag boundary conditions. Peak injury metrics were also elevated with close-proximity occupants relative to other boundary conditions. The computational model indicated decreasing influence of air bag on compression metrics with increasing ΔV. Air bag influence on viscous metric was greatest with close-proximity occupants at ΔV = 7.0 m/s, at which the response magnitude was greater than linear summation of metrics resulting from rigid impact and stationary close-proximity interaction. Conclusions: These results suggest that stationary close-proximity occupants may not represent the only scenario of side air bag deployment harmful to the thoraco-abdominal region. The sensitivity of the viscous metric and implications for visceral trauma are also discussed.

Vibration Excitation and Control of a Pedestrian Walkway by Individuals and Crowds

As part of a continuing study on effects of humans on loading and dynamic response of footbridges, a steel frame walkway has been the subject of studies on the effects of multiple pedestrians with respect to loading and response mitigation. Following finite element modeling and experimental modal analysis to identify the low frequency vibration modes likely to be excited by normal walking, the variation of response with pedestrian density and of system damping and natural frequency with occupancy by stationary pedestrians were both studied. The potentially mitigating effect of stationary occupants is still not well understood and the study included direct measurement of damping forces and absorbed energy using a force plate. The various tests showed that energy dissipation measured directly was consistent with the observed change in damping, that vertical and lateral response both varied approximately with square root of number of pedestrians, and that the simple model of a human as a single mass‐spring‐damper system may need to be refined to fit observed changes in modal parameters with a crowd of humans present. Modal parameter changes with moving pedestrians were small compared to those with stationary pedestrians indicating that within limits, modal parameters for the empty structure could be used in analysis.