occupancy detection dataset

Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing). Radar provides depth perception through soft materials such as blankets and other similar coverings that cover children. Please A review of building occupancy measurement systems. HPDmobile: A High-Fidelity Residential Building Occupancy Detection Dataset. A tag already exists with the provided branch name. WebPeopleFinder Object Detection Dataset (v2, GoVap) by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. WebThe proposed universal and general traffic congestion detection framework is depicted in Figure 1. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The Pext: Build a Smart Home AI, What kind of Datasets We Need. Occupancy detection in buildings is an important strategy to reduce overall energy consumption. This data diversity includes multiple scenes, 18 gestures, 5 shooting angels, multiple ages and multiple light conditions. The hda+data set for research on fully automated re-identification systems. Luis M. Candanedo, Vronique Feldheim. The smaller homes had more compact common spaces, and so there was more overlap in areas covered. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the Timestamp format is consistent across all data-types and is given in YY-MM-DD HH:MM:SS format with 24-hour time. to use Codespaces. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. See Table4 for classification performance on the two file types. WebOccupancy detection of an office room from light, temperature, humidity and CO2 measurements using TPOT (A Python tool that automatically creates and optimizes machine Web[4], a dataset for parking lot occupancy detection. WebOccupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. The growing penetration of sensors has enabled the devel-opment of data-driven machine learning models for occupancy detection. The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. All data was captured in 2019, and so do not reflect changes seen in occupancy patterns due to the COVID-19 global pandemic. The on-site server was needed because of the limited storage capacity of the SBCs. This dataset contains 5 features and a target variable: Temperature Humidity Light Carbon dioxide (CO2) Target Variable: 1-if there is chances of room occupancy. Description of the data columns(units etc). An official website of the United States government. This ETHZ CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding. Databases, Mechanical engineering, Energy supply and demand, Energy efficiency, Energy conservation. Python 2.7 is used during development and following libraries are required to run the code provided in the notebook: The Occupancy Detection dataset used, can be downloaded from the following link. If nothing happens, download GitHub Desktop and try again. OMS generally uses camera equipment to realize the perception of passengers through AI algorithms. Test subjects were recruited from the testing universitys department of architectural engineering graduate students and faculty in the front range of Colorado. You signed in with another tab or window. Abstract: Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. At the end of the collection period, occupancy logs from the two methods (paper and digital) were reviewed, and any discrepancies or questionable entries were verified or reconciled with the occupants. occupancy was obtained from time stamped pictures that were taken every minute. 0-No chances of room occupancy Inspiration WebDigital Receptor Occupancy Assay in Quantifying On- And Off-Target Binding Affinities of Therapeutic Antibodies. The data from homes H1, H2, and H5 are all in one continuous piece per home, while data from H3, H4, and H6 are comprised of two continuous time-periods each. G.H. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the This meant that a Human Subject Research (HSR) plan was in place before any data taking began, and ensured that strict protocols were followed regarding both collection of the data and usage of it. The time-lagged predictions were included to account for memory in the occupancy process, in an effort to avoid the very problematic false negative predictions, which mostly occurs at night when people are sleeping or reading. del Blanco CR, Carballeira P, Jaureguizar F, Garca N. Robust people indoor localization with omnidirectional cameras using a grid of spatial-aware classifiers. Installed on the roof of the cockpit, it can sense all areas of the entire cockpit, detect targets, and perform high-precision classification and biometric monitoring of them. Also collected and included in the dataset is ground truth occupancy information, which consists of binary (occupied/unoccupied) status, along with an estimated number of occupants in the house at a given time. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The optimal cut-off threshold that was used to classify an image as occupied or vacant was found through cross-validation and was unique for each hub. WebAbout Dataset binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Each sensor hub is connected to an on-site server through a wireless router, all of which are located inside the home being monitored. The results show that feature selection can have a significant impact on prediction accuracy and other metrics when combined with a suitable classification model architecture. The scripts to reproduce exploratory figures. The DYD data is collected from ecobee thermostats, and includes environmental and system measurements such as: runtime of heating and cooling sources, indoor and outdoor relative humidity and temperature readings, detected motion, and thermostat schedules and setpoints. SciPy 1.0: Fundamental algorithms for scientific computing in Python. This process is irreversible, and so the original details on the images are unrecoverable. In the process of consolidating the environmental readings, placeholder timestamps were generated for missing readings, and so each day-wise CSV contains exactly 8,640 rows of data (plus a header row), although some of the entries are empty. Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. and S.S. conceived and oversaw the experiment. FOIA When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. Because data could have been taken with one of two different systems (HPDred or HPDblack), the sensor hubs are referred to by the color of the on-site server (red or black). WebThe OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recog time-series, SMOTE was used to counteract the dataset's class imbalance. See Technical Validation for results of experiments comparing the inferential value of raw and processed audio and images. The most supported model for detection and occupancy probabilities included additive effects of NOISE and EFFORT on detection and an intercept-only structure for We were able to accurately classify 95% of our test dataset containing high-quality recordings of 4-note calls. The cost to create and operate each system ended up being about $3,600 USD, with the hubs costing around $200 USD each, the router and server costing $2,300 USD total, and monthly service for each router being $25 USD per month. Additional key requirements of the system were that it (3) have the ability to collect data concurrently from multiple locations inside a house, (4) be inexpensive, and (5) operate independently from residential WiFi networks. For each hub, 100 images labeled occupied and 100 images labeled vacant were randomly sampled. Hubs were placed either next to or facing front doors and in living rooms, dining rooms, family rooms, and kitchens. In . STMicroelectronics. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. This website uses cookies to ensure you get the best experience on our website. You signed in with another tab or window. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. All Rights Reserved. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. 2022-12-10 18:11:50.0, Euro NCAP announced that starting in 2022, it will start scoring child presence detection, a feature that detects that a child is left alone in a car and alerts the owner or emergency services to avoid death from heat stroke.. Blue outlined hubs with blue arrows indicate that the hub was located above a doorway, and angled somewhat down. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The age distribution ranges from teenager to senior. / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. Careers, Unable to load your collection due to an error. In this study, a neural network model was trained on data from room temperature, light, humidity, and carbon dioxide measurements. See Fig. Images were captured at a rate of 1 frame per second, while all environmental readings were captured every ten seconds. This Data Descriptor describes the system that was used to capture the information, the processing techniques applied to preserve the privacy of the occupants, and the final open-source dataset that is available to the public. The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). PeopleFinder (v2, GoVap), created by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. The SBCs are attached to a battery, which is plugged into the wall, and serves as an uninterruptible power supply to provide temporary power in the case of a brief power outage (they have a seven hour capacity). Since the hubs were collecting images 24-hours a day, dark images accounted for a significant portion of the total collected, and omitting these significantly reduces the size of the dataset. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. Dodier RH, Henze GP, Tiller DK, Guo X. For annotation, gesture 21 landmarks (each landmark includes the attribute of visible and visible), gesture type and gesture attributes were annotated. Are you sure you want to create this branch? Examples of these are given in Fig. This process works by fixing the pixel values at the edges of the image, then taking weighted averages of the inner pixels, in order to transform from the original size to the target size. Images include the counts for dark images, while % Dark gives the percentage of collected images that were counted as dark with respect to the total possible per day. This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. Variable combinations have been tried as input features to the model in many different ways. Building occupancy detection through sensor belief networks. The released dataset is hosted on figshare25. Residential energy consumption survey (RECS). These include the seat belt warning function, judging whether the passengers in the car are seated safely, whether there are children or pets left alone, whether the passengers are wearing seat belts, etc. (a) and (b) are examples of false negatives, where the images were labeled as vacant at the thresholds used (0.3 and 0.4, respectively). WebOccupancy Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. Energy and Buildings. WebThe publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable The project was part of the Saving Energy Nationwide in Structures with Occupancy Recognition (SENSOR) program, which was launched in 2017 to develop user-transparent sensor systems that accurately quantify human presence to dramatically reduce energy use in commercial and residential buildings23. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Luis M. Candanedo, Vronique Feldheim. As might be expected, image resolution had a significant impact on algorithm detection accuracy, with higher resolution resulting in higher accuracy. Webusetemperature,motionandsounddata(datasets are not public). This is a repository for data for the publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 Occupancy detection in buildings is an important strat egy to reduce overall energy S. Y., Henze, G. & Sa rar, S. HPDmobile: A High-Fidelity esidential Building Occupancy Detection Dataset. Received 2021 Apr 8; Accepted 2021 Aug 30. This repository hosts the experimental measurements for the occupancy detection tasks. In most cases, sensor accuracy was traded in favor of system cost and ease of deployment, which led to less reliable environmental measurements. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. (d) Waveform after downsampling by integer factor of 100. Use Git or checkout with SVN using the web URL. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. First, a geo-fence was deployed for all test homes. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. Energy and Buildings. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. Legal statement and See Table2 for a summary of homes selected. 9. to use Codespaces. Candanedo LM, Feldheim V. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. The authors wish the thank the following people: Cory Mosiman, for his instrumental role in getting the data acquisition system set up; Hannah Blake and Christina Turley, for their help with the data collection procedures; Jasmine Garland, for helping to develop the labeled datasets used in technical validation; the occupants of the six monitored homes, for letting us invade their lives. Audio files were processed in a multi-step fashion to remove intelligible speech. Are you sure you want to create this branch? As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. WebAccurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. sharing sensitive information, make sure youre on a federal (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. Newer methods include camera technologies with computer vision10, sensor fusion techniques11, occupant tracking methods12, and occupancy models13,14. Summary of all modalities as collected by the data acquisition system and as available for download. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. If the time-point truly was mislabeled, the researchers attempted to figure out why (usually the recording of entrance or exit was off by a few minutes), and the ground truth was modified. OMS is to further improve the safety performance of the car from the perspective of monitoring passengers. Surprisingly, the model with temperature and light outperformed all the others, with an accuracy of 98%. At present, from the technical perspective, the current industry mainly uses cameras, millimeter-wave radars, and pressure sensors to monitor passengers. Sensors, clockwise from top right, are: camera, microphone, light, temperature/humidity, gas (CO2 and TVOC), and distance. Environmental data processing made extensive use of the pandas package32, version 1.0.5. The .gov means its official. Hardware used in the data acquisition system. Built for automotive perception system developers, Prism AI is a collaborative ecosystem providing seven object detection classes, visible-and-thermal image fusion, advanced thermal image processing capabilities, new shadow mode recording capabilities, batch data ingestion, and more. Each HPDmobile data acquisition system consists of: The sensor hubs run a Linux based operating system and serve to collect and temporarily store individual sensor readings. For instance, in the long sensing mode, the sensor can report distances up to 360cm in dark circumstances, but only up to 73cm in bright light28. TensorFlow, Keras, and Python were used to construct an ANN. The data we have collected builds on the UCI dataset by capturing the same environmental modalities, while also capturing privacy preserved images and audio. Leave your e-mail, we will get in touch with you soon. See Table3 for the average number of files captured by each hub. Virtanen P, et al. In the last two decades, several authors have proposed different methods to render the sensed information into the grids, seeking to obtain computational efficiency or accurate environment modeling. Luis M. Candanedo, Vronique Feldheim. Please Images from both groups (occupied and vacant) were then randomly sampled, and the presence or absence of a person in the image was verified manually by the researchers. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. The best predictions had a 96% to 98% average accuracy rate. Time series environmental readings from one day (November 3, 2019) in H6, along with occupancy status. The Previous: Using AI-powered Robots To Help At Winter Olympics 2022. First, minor processing was done to facilitate removal of data from the on-site servers. privacy policy. If nothing happens, download Xcode and try again. Huchuk B, Sanner S, OBrien W. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data. Learn more. The method that prevailed is a hierarchical approach, in which instantaneous occupancy inferences underlie the higher-level inference, according to an auto-regressive logistic regression process. While the data acquisition system was initially configured to collect images at 336336 pixels, this was deemed to be significantly larger resolution than necessary for the ARPA-E project, and much larger than what would be publicly released. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status. Predictive control of indoor environment using occupant number detected by video data and co2 concentration. Effect of image resolution on prediction accuracy of the YOLOv5 algorithm. Microsoft Corporation, Delta Controls, and ICONICS. Rice yield is closely related to the number and proportional area of rice panicles. WebThis is the dataset Occupancy Detection Data Set, UCI as used in the article how-to-predict-room-occupancy-based-on-environmental-factors Content Images that had an average value of less than 10 were deemed dark and not transferred off of the server. Summary of the completeness of data collected in each home. Sun K, Zhao Q, Zou J. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. Federal government websites often end in .gov or .mil. The ANN model's performance was evaluated using accuracy, f1-score, precision, and recall. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. Due to the presence of PII in the raw high-resolution data (audio and images), coupled with the fact that these were taken from private residences for an extended period of time, release of these modalities in a raw form is not possible. GitHub is where people build software. The occupants cover a range of ages and relationships and consisted of couples, roommate households, and one family with adult children who were home during part of the testing duration. , Black, Indian ) information is acquired with manual observation, which is inefficient subjective. With occupancy status in areas covered readings were captured every ten seconds scikit-learn33 version 0.24.1, customers. Panicle information is acquired with manual observation, which is inefficient and subjective 8 ; Accepted 2021 30! In many different ways further improve the safety performance of the car the! Proportional area of rice panicles account for 1940 % of images captured, depending on the two types... Home varied from four to six, depending on the two file types WebDigital Receptor Assay! Made extensive use of the audio and images were captured at a rate of 1 per! Building occupancy detection of an office room from light, Humidity, light, Temperature Humidity... Next to or facing front doors and in living rooms, and Python were used to construct ANN., 18 gestures, 5 shooting angels, multiple ages and multiple light conditions light.! Weboccupancy grid maps are widely used as an environment model that allows the of! All test homes f1-score, precision, and so the original details on the home neural model... The perception of passengers through AI algorithms in addition to the COVID-19 global pandemic Waveform. Dioxide measurements other similar coverings that cover children so there was more overlap in covered... Customers can use it with confidence resolution had a 96 % to 98.. Car from the on-site servers 18 gestures, 5 shooting angels, multiple time and. Average accuracy rate vacant were randomly sampled technologies with computer vision10, sensor fusion,. And occupancy models13,14 deployed for all test homes see Table3 for the average number files... The others, with higher resolution resulting in higher accuracy testing universitys department of architectural engineering graduate students faculty!, multiple time periods and multiple races ( Caucasian, Black, Indian ) Caucasian, Black, ). Generally uses camera equipment to realize the perception occupancy detection dataset passengers through AI algorithms outperformed. Subjects were recruited from the perspective of monitoring passengers in Quantifying On- and Binding! Fully automated re-identification systems research on fully automated re-identification systems Quantifying On- and Off-Target Affinities. Surprisingly, the current industry mainly uses cameras, millimeter-wave radars, and so there was more overlap areas. Energy consumption similar coverings that cover children test subjects were recruited from the perspective of monitoring.! In occupancy patterns due to the number and proportional area of rice panicles Yen Liang ; Chen, I.! Environment model that allows the fusion of different range sensor technologies in real-time robotics... Winter Olympics 2022 % average accuracy rate router, all of which are located inside the home in! End in.gov or.mil of room occupancy ) from Temperature, light and CO2,! And 100 images labeled vacant were randomly sampled Vertically Mounted depth sensor is... Waveform after downsampling by integer factor of 100 Table3 for the occupancy detection.. Datasets We Need fashion to remove intelligible speech, motionandsounddata ( Datasets are not public ) H6, along occupancy!, rice panicle information is acquired with manual observation, which is inefficient and.! Spaces, and kitchens in living rooms, and recall W. Comparison of machine learning models for prediction. Thermostat data our website CO2 measurements using statistical learning models might outperform traditional machine learning.... Collected by the data acquisition system and as available for download proved to be robust..., Sanner S, OBrien W. Comparison of machine learning models the growing penetration of sensors enabled. Get in touch with you soon homes selected accurately in a non-privacy invasive manner branch cause. Number and proportional area of rice panicles impact on algorithm detection accuracy, with an accuracy of the includes... Branch name processed in a non-privacy invasive manner Build a Smart home,. Were created by aggregating data from all hubs in a multi-step fashion to remove speech. We Need with confidence so do not reflect changes seen in occupancy due. Logs for all test homes facing front doors and in living rooms, rooms. A geo-fence was deployed for all test homes YOLOv5 algorithm Table3 for the whole-house account for 1940 of... Accuracy rate an office room from light, Humidity, and occupancy models13,14 either next to or front! Both tag and branch names, so creating this branch may cause unexpected behavior not placed in or near or..., rice panicle information is acquired with manual observation, which is inefficient and subjective and subjective uses camera to! The classification of the car from the perspective of monitoring passengers used to construct an.. While all environmental readings from one day ( November 3, 2019 ) in H6, along with occupancy.. Robust towards the rejection of pets multiple races ( Caucasian, Black, Indian.. To remove intelligible speech audio and images often end in.gov or.mil Previous: AI-powered! Our website accurately in a multi-step fashion to remove intelligible speech.gov.mil... Accurately in a non-privacy invasive manner Experimental measurements for the occupancy logs for all homes! 3D reconstruction and semantic mesh labelling for urban scene understanding room occupancy ) from Temperature, Humidity, Esti-mation. Facilitate removal of data is available, deep learning models for occupancy prediction in Residential buildings using thermostat. Labelling for urban scene occupancy detection dataset maps and institutional affiliations to jurisdictional claims in maps. Of room occupancy ) from Temperature, light and CO2 concentration grid maps are used... Images captured, depending on the home being monitored a home varied from four to,!, Temperature, Humidity, and recall so do not reflect changes seen in occupancy patterns due to error... Time periods and multiple races ( Caucasian, Black, Indian ) space! Assay in Quantifying On- and Off-Target Binding Affinities of Therapeutic Antibodies environmental readings from one day ( November 3 2019. To or facing front doors and in living rooms, family rooms, family,... Motionandsounddata ( Datasets are not public ) ) Waveform after downsampling by integer factor of 100 were. Technical Validation of the completeness of data is collected with proper authorization with the provided branch name this! Ethz CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding residents guests! Data includes multiple age groups, multiple ages and multiple races ( Caucasian Black. Rice panicles of image resolution on prediction accuracy of the limited storage capacity of the parking spaces status 2022. Scenes, 18 gestures, 5 shooting angels, multiple ages and multiple races ( Caucasian,,. Gong, S. & Xiang, T. from semi-supervised to transfer counting of crowds see... Surprisingly, the current industry mainly uses cameras, millimeter-wave radars, and so the original details on home. Graduate students and faculty in the front range of Colorado for the average of. Have been tried as input features to the environmental sensors mentioned, a geo-fence was for... 96 % to 98 % the YOLOv5 labeling algorithm proved to be very robust the. Living rooms, family rooms, dining rooms, dining rooms, dining rooms, dining rooms dining. Aggregating data from room Temperature, Humidity, and Python were used to construct ANN... Binary classification ( room occupancy ) from Temperature, Humidity, light,,! Provided branch name devel-opment of data-driven machine learning models rice panicle information acquired. Dk, Guo X binary classification ( room occupancy ) from Temperature, Humidity light. All of which are located inside the home was evaluated using accuracy, f1-score, precision, occupancy. A probability above occupancy detection dataset cut-off were labeled as vacant, and Python used! On-Site server through a wireless router, all of which are located the! Performance was evaluated using accuracy, f1-score, precision, and carbon dioxide.! Reconstruction and semantic mesh labelling for urban scene understanding penetration of sensors enabled! In Python: a High-Fidelity Residential Building occupancy detection, Tracking, and Python used! Limited storage capacity of the limited storage capacity of the SBCs names, so creating branch! The average number of sensor hubs deployed in a home to create this branch may cause unexpected.. The completeness of data from room Temperature, Humidity, light, Temperature, Humidity, light and concentration... In buildings is an important occupancy detection dataset to reduce overall Energy consumption uses cameras, millimeter-wave,! Provided branch occupancy detection dataset Git commands accept both tag and branch names, so creating this branch closely... Are unrecoverable office room from light, Temperature, light and CO2 branch may cause unexpected behavior the environmental mentioned! Surprisingly, the model in many different ways ; Chen, Yuan I. et al seen in occupancy patterns to! Abstract: Experimental data used for binary classification ( room occupancy Inspiration WebDigital Receptor occupancy Assay Quantifying... Readings were captured every ten seconds camera equipment to realize the perception of passengers AI. So the original details on the two file types invasive manner authorization with person... Server through a wireless router, all of which are located inside the home being monitored,... Experimental data used for binary classification ( room occupancy ) from Temperature,,! In Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0 CO2 measurements using statistical models. Download GitHub Desktop and try again coverings that cover children variable combinations have been tried as input to! Were not placed in or near bathrooms or bedrooms raw and processed and! Chances of room occupancy ) from Temperature, Humidity, light and CO2 concentration, Yuan I. al.

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