Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants

A Low-Cost Control Occupancy Solution Using a Time-of-Flight Ranging Sensor Laser

Forma part de la Conferència IPIN 2021 WiP Proceedings, November 29 – December 2, 2021, Lloret de Mar, Spain "International Conference on Indoor Positioning and Indoor Navigation, IPIN 2021 [Preface]," 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2021, doi: 10.1109/IPIN51156.2021.9701845.The pandemic situation has driven to several measures to prevent the spread of COVID-19. One of these measures is social distance and, as a consequence, limitation of capacity of indoor closed spaces. This makes necessary the deployment of systems that help to control occupancy of spaces. This work proposes a low-cost system to control access to an indoor closed space with a single door. The system is based in a two laser Time-of-Flight sensors VL53L0X over a HiLetgo UNO R3D1R32 ESP32 microcontroller. The system counts the occupancy of the room and share it with a database and a dashboard, using Node-RED. The tested prototype shows a 86.6% reliability that increases to a 100% reliability when users are informed to enter or exit one by one. The main contributions of this work are: to control capacity of one-entrance indoor closed space with a low cost open system; and to record occupancy of the room in order to analyse it behaviour with time

Building Occupancy Estimation Using machine learning algorithms

Building occupancy recently has drawn the attention of many researchers. With the advance of new technologies in AI and IoT, it has become possible to further optimize building energy consumption without compromising comfort of the occupants. In this thesis project, occupancy is estimated by training models on data collected from the building called Arkivenshus in Stavanger. The data collected includes measurements of electricity consumption, ventilation, hot and cold-water consumption and PIR sensors (Passive infra-red sensors). The models that are trained are classification algorithms such as KNN, decision tree, random forest, and support vector machine. Data from the building is collected over two months period where data points are collected every 15min. Occupancy detection solutions that employ cameras, WIFI activities etc can be used to detect occupancy in buildings, however these solutions can be intrusive, costly and computationally expensive. Moreover, PIR sensors which are used for activation of lighting systems detect occupancy, they however cannot be directly related to the count of number of people. To estimate the number of people inside building I have labelled the data in five categories, where 1 represents counts less than 5, 2 represents between 5 and 25,3 represents between 25 and 50, 4 represents between 50 and 75 and for counts greater than 75 they are represented by class 5. Due to the pandemic I was not able to register number of people inside the building more than 80, which presumably has an impact on the efficiency of my model. The performance of the models are compared using various metrices, Since the data is nor balanced and I have divided the target into five classes, looking only the accuracy of a model is a bit misleading in selecting the best model. Considering accuracy, confusion matrix and learning curves of each model the best performing model is found to be SVM (Support vector machine)

On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations)

Intruder Localization and Tracking Using Two Pyroelectric Infrared Sensors

In this paper, we introduce a method to estimate the range of an intruder and track its trajectory by utilizing the received signal strength of the heat flux for pyroelectric infrared (PIR) sensors. To this end, we first develop a mathematical model of the received heat flux signal strength and the corresponding PIR signal for a moving intruder. The algorithm uses only two PIR sensors and the geometry of the field of views (FOVs) to perform the estimation and tracking process without any knowledge of the intruder's parameters. The tracking algorithm shows remarkable performance in estimating the intruder's parameters. The intruder heat flux was accurately estimated even at large separation distances as was the intruder path angle. Finally, the intruder's location was also very accurately estimated with sub-meter error for large separation distances

Data Processing for Device-Free Fine-Grained Occupancy Sensing Using Infrared Sensors

Fine-grained occupancy information plays an essential role for various emerging applications in smart homes, such as personalized thermal comfort control and human behavior analysis. Existing occupancy sensors, such as passive infrared (PIR) sensors generally provide limited coarse information such as motion. However, the detection of fine-grained occupancy information such as stationary presence, posture, identification, and activity tracking can be enabled with the advance of sensor technologies. Among these, infrared sensing is a low-cost, device-free, and privacy-preserving choice that detects the fluctuation (PIR sensors) or the thermal profiles (thermopile array sensors) from objects' infrared radiation. This work focuses on developing data processing models towards fine-grained occupancy sensing using the synchronized low-energy electronically chopped PIR (SLEEPIR) sensor or the thermopile array sensors. The main contributions of this dissertation include: (1) creating and validating the mathematical model of the SLEEPIR sensor output towards stationary occupancy detection; (2) developing the SLEEPIR detection algorithm using statistical features and long-short term memory (LSTM) deep learning; (3) building machine learning framework for posture detection and activity tracking using thermopile array sensors; and (4) creating convolutional neural network (CNN) models for facing direction detection and identification using thermopile array sensors

How Low Can You Go? Performance Trade-offs in Low-Resolution Thermal Sensors for Occupancy Detection: A Systematic Evaluation

We contribute by systematically analysing the performance trade-offs, costs (privacy loss and deployment cost) and limits of low-resolution thermal array sensors for occupancy detection. First, to assess performance limits, we manipulate the frame rate and resolution of images to establish the lowest possible values where reliable occupancy information can be captured. We also assess the effect of different viewing angles on the performance. We analyse performance using two datasets, an open-source dataset of thermal array sensor measurements (TIDOS) and a proprietary dataset that is used to validate the generality of the findings and to study the effect of different viewing angles. Our results show that even cameras with a 4x2 resolution - significantly lower than what has been used in previous research - can support reliable detection, as long as the frame rate is at least 4 frames per second. The lowest tested resolution, 2x2, can also offer reliable detection rates but requires higher frame rates (at least 16 frames per second) and careful adjustment of the camera viewing angle. We also show that the performance is sensitive to the viewing angle of the sensor, suggesting that the camera's field-of-view needs to be carefully adjusted to maximize the performance of low-resolution cameras. Second, in terms of costs, using a camera with only 4x2 resolution reveals very few insights about the occupants' identity or  behaviour, and thus helps to preserve their privacy. Besides privacy, lowering the resolution and frame rate decreases manufacturing and operating costs and helps to make the solution easier to adopt. Based on our results, we derive guidelines on how to choose sensor resolution in real-world deployments by carrying out a small-scale trade-off analysis that considers two representative buildings as potential deployment areas and compares the cost, privacy and accuracy trade-offs of different resolutions.Peer reviewe

Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model:Experimental Validation in a Residential Building

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Design of a new method for detection of occupancy in the smart home using an FBG sensor

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.Web of Science202art. no. 39