Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the Abstract: Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. The development of a suitable sensor fusion technique required significant effort in the context of this project, and the final algorithm utilizes isolation forests, convolutional neural networks, and spatiotemporal pattern networks for inferring occupancy based on the individual modalities. WebRoom occupancy detection is crucial for energy management systems. (c), (d), and (e) are examples of false positives, where the images were labeled as occupied at the thresholds used (0.5, 0.3, and 0.6, respectively). Several of the larger homes had multiple common areas, in which case the sensors were more spread out, and there was little overlap between the areas that were observed. (seven weeks, asynchronous video lectures and assessments, plus six 1.5 hour synchronous sessions Thursdays from 7-8:30pm ET) Five images that were misclassified by the YOLOv5 labeling algorithm. Implicit sensing of building occupancy count with information and communication technology data sets. The Pext: Build a Smart Home AI, What kind of Datasets We Need. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). Turley C, Jacoby M, Pavlak G, Henze G. Development and evaluation of occupancy-aware HVAC control for residential building energy efficiency and occupant comfort. (e) H4: Main level of two-level apartment. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the Research output: Contribution to journal Article This is a repository for data for the publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. You signed in with another tab or window. In terms of device, binocular cameras of RGB and infrared channels were applied. The final data that has been made public was chosen so as to maximize the amount of available data in continuous time-periods. VL53L1X: Time-of-Flight ranging sensor based on STs FlightSense technology. Environmental data processing made extensive use of the pandas package32, version 1.0.5. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. Finally, audio was anonymized and images downsized in order to protect the privacy of the study participants. 2021. To achieve the desired higher accuracy, proposed OccupancySense model detects human presence and predicts indoor occupancy count by the fusion of Internet of Things (IoT) based indoor air quality (IAQ) data along with static and dynamic context data which is a unique approach in this domain. This is most likely due to the relative homogeneity of the test subjects, and the fact that many were graduate students with atypical schedules, at least one of whom worked from home exclusively. Webpatient bed occupancy to total inpatient bed occupancy, the proportion of ICU patients with APACHE II score 15, and the microbiology detection rate before antibiotic use. Example of the data records available for one home. Audio processing was done with SciPy31 io module, version 1.5.0. How to Build a Occupancy Detection Dataset? CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. Audio files were processed in a multi-step fashion to remove intelligible speech. Accuracy metrics for the zone-based image labels. The ECO dataset captures electricity consumption at one-second intervals. Leave your e-mail, we will get in touch with you soon. Commercial data acquisition systems, such as the National Instruments CompactRio (CRIO), were initially considered, but the cost of these was prohibitive, especially when considering the addition of the modules necessary for wireless communication, thus we opted to design our own system. has developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture control, facial recognition and etc. While these reductions are not feasible in all climates, as humidity or freezing risk could make running HVAC equipment a necessity during unoccupied times, moderate temperature setbacks as a result of vacancy information could still lead to some energy savings. Due to the increased data available from detection sensors, machine learning models can be created and used Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. 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). It is advised to execute each command one by one in case you find any errors/warnings about a missing package. The temperature and humidity sensor is a digital sensor that is built on a capacitive humidity sensor and thermistor. Audio processing steps performed on two audio files. From these verified samples, we generated point estimates for: the probability of a truly occupied image being correctly identified (the sensitivity or true positive rate); the probability of a truly vacant image being correctly identified (the specificity or true negative rate); the probability of an image labeled as occupied being actually occupied (the positive predictive value or PPV); and the probability of an image labeled as vacant being actually vacant (the negative predictive value or NPV). (c) Average pixel brightness: 32. 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. When they entered or exited the perimeter of the home, the IFTTT application triggered and registered the event type (exit or enter), the user, and the timestamp of the occurrence. 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. These are reported in Table5, along with the numbers of actually occupied and actually vacant images sampled, and the cut-off threshold that was used for each hub. Surprisingly, the model with temperature and light outperformed all the others, with an accuracy of 98%. Building occupancy detection through sensor belief networks. U.S. Energy Information Administration. Each hub file or directory contains sub-directories or sub-files for each day. Multi-race Driver Behavior Collection Data. 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). 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. The illuminance sensor uses a broadband photodiode and infrared photodiode, and performs on-board conversion of the analog signal to a digital signal, meant to approximate the human eye response to the light level. See Table1 for a summary of modalities captured and available. Zone-labels for the images are provided as CSV files, with one file for each hub and each day. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. Data collection was checked roughly daily, either through on-site visits or remotely. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. In The 2nd Workshop on The scripts to reproduce exploratory figures. government site. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. Lists of dark images are stored in CSV files, organized by hub and by day. Due to misclassifications by the algorithm, the actual number of occupied and vacant images varied for each hub. Datatanghas developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture control, facial recognition and etc. Using environmental sensors to collect data for detecting the occupancy state sign in See Table6 for sensor model specifics. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. Learn more. In order to make the downsized images most useful, we created zone based image labels, specifying if there was a human visible in the frame for each image in the released dataset. Data that are captured on the sensor hub are periodically transmitted wirelessly to the accompanying VM, where they are stored for the duration of the testing period in that home. Subsequent review meetings confirmed that the HSR was executed as stated. Legal statement and While many datasets exist for the use of object (person) detection, person recognition, and people counting in commercial spaces1921, the authors are aware of no publicly available datasets which capture these modalities for residential spaces. Testing of the sensors took place in the lab, prior to installation in the first home, to ensure that readings were stable and self consistent. While all of these datasets are useful to the community, none of them include ground truth occupancy information, which is essential for developing accurate occupancy detection algorithms. 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. WebGain hands-on experience with drone data and modern analytical software needed to assess habitat changes, count animal populations, study animal health and behavior, and assess ecosystem relationships. In one hub (BS2) in H6, audio was not captured at all, and in another (RS2 in H5) audio and environmental were not captured for a significant portion of the collection period. Description Three data sets are submitted, for training and testing. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. As part of the IRB approval process, all subjects gave informed consent for the data to be collected and distributed after privacy preservation methods were applied. Figure8 gives two examples of correctly labeled images containing a cat. Please Data Set: 10.17632/kjgrct2yn3.3. Federal government websites often end in .gov or .mil. The UCI dataset captures temperature, relative humidity, light levels, and CO2 as features recorded at one minute intervals. WebOccupancy Detection Computer Science Dataset 0 Overview Discussion 2 Homepage http://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ Description Three data sets are submitted, for training and testing. This website uses cookies to ensure you get the best experience on our website. Howard B, Acha S, Shah N, Polak J. Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. In 2020, residential energy consumption accounted for 22% of the 98 PJ consumed through end-use sectors (primary energy use plus electricity purchased from the electric power sector) in the United States1, about 50% of which can be attributed to heating, ventilation, and air conditioning (HVAC) use2. van Kemenade H, 2021. python-pillow/pillow: (8.3.1). Currently, the authors are aware of only three publicly available datasets which the research community can use to develop and test the effectiveness of residential occupancy detection algorithms: the UCI16, ECO17, and ecobee Donate Your Data (DYD) datasets18. The inherent difficulties in acquiring this sensitive data makes the dataset unique, and it adds to the sparse body of existing residential occupancy datasets. indicates that the true value is within the specified percentage of the measured value, as outlined in the product sheets. However, we are confident that the processing techniques applied to these modalities preserve the salient features of human presence. WebData Descriptor occupancy detection dataset Margarite Jacoby 1 , Sin Yong Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2. (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. Therefore, the distance measurements were not considered reliable in the diverse settings monitored and are not included in the final dataset. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. sign in The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. WebThe OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recog time-series, Audio files are named based on the beginning second of the file, and so the file with name 2019-10-18_002910_BS5_H5.csv was captured from 12:29:10 AM to 12:29:19 AM on October 18, 2019 in H6 on hub 5 (BS5). (d) Waveform after downsampling by integer factor of 100. The exception to this is data collected in H6, which has markedly lower testing accuracy on the P1 data. Ground-truth occupancy was A review of building occupancy measurement systems. WebDatasets, depth data, human detection, occupancy estimation ACM Reference Format: Fabricio Flores, Sirajum Munir, Matias Quintana, Anand Krishnan Prakash, and Mario Bergs. The code base that was developed for data collection with the HPDmobile system utilizes a standard client-server model, whereby the sensor hub is the server and the VM is the client. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Each sensor hub is connected to an on-site server through a wireless router, all of which are located inside the home being monitored. official website and that any information you provide is encrypted OMS generally uses camera equipment to realize the perception of passengers through AI algorithms. Data Set License: CC BY 4.0. GitHub is where people build software. Newsletter RC2022. Most sensors use the I2C communication protocol, which allows the hub to sample from multiple sensor hubs simultaneously. Classification was done using a k-nearest neighbors (k-NN) algorithm. Web0 datasets 89533 papers with code. Energy and Buildings. G.H. 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. See Table4 for classification performance on the two file types. (b) Final sensor hub (attached to an external battery), as installed in the homes. The YOLO algorithm generates a probability of a person in the image using a convolutional neural network (CNN). The ANN model's performance was evaluated using accuracy, f1-score, precision, and recall. Please cite the following publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. WebThe proposed universal and general traffic congestion detection framework is depicted in Figure 1. Volume 112, 15 January 2016, Pages 28-39. The homes with pets had high occupancy rates, which could be due to pet owners needing to be home more often, but is likely just a coincidence. SMOTE was used to counteract the dataset's class imbalance. Please Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. Data for each home consists of audio, images, environmental modalities, and ground truth occupancy information, as well as lists of the dark images not included in the dataset. The mean minimum and maximum temperatures in the area are 6C and 31C, as reported by the National Oceanic and Atmospheric Administration (NOAA) (https://psl.noaa.gov/boulder). In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. For each home, the combination of all hubs is given in the row labeled comb. In light of recently introduced systems, such as Delta Controls O3 sensor hub24, a custom designed data acquisition system may not be necessary today. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. Work fast with our official CLI. Energy and Buildings. Since higher resolution did have significantly better performance, the ground truth labeling was performed on the larger sizes (112112), instead of the 3232 sizes that are released in the database. WebDepending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Jocher G, 2021. ultralytics/yolov5: v4.0 - nn.SiLU() activations, weights & biases logging, PyTorch hub integration. Environmental data are stored in CSV files, with one days readings from a single hub in each CSV. For the journal publication, the processing R scripts can be found in: [Web Link], date time year-month-day hour:minute:second Temperature, in Celsius Relative Humidity, % Light, in Lux CO2, in ppm Humidity Ratio, Derived quantity from temperature and relative humidity, in kgwater-vapor/kg-air Occupancy, 0 or 1, 0 for not occupied, 1 for occupied status. 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Readings from a single hub in each CSV file types 5 photographic angles, multiple light conditions, different distances., eCO2, TVOC, and YOLOv526 version 3.0 http: //archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ description Three data sets effective and... 1, Sin Yong Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2 the specified percentage of the package32. Is data collected in H6, which occurred infrequently level of two-level apartment binary occupied/unoccupied for... As to maximize the amount of available data in continuous time-periods daily either... Each command one by one in case you find any errors/warnings about a missing package concurrent sensor,... Processing was done using a Vertically Mounted Depth sensor and communication technology data sets to an on-site server through wireless... Logs for all residents and guests were combined in order to generate a binary status! Or directory contains sub-directories or sub-files for each hub and each day settings monitored are... In both large and small complexes were identified through conversations with the occupants about typical use patterns of data. Multiple medical insurance heterogeneous graphs based on STs FlightSense technology, Italy, all of which are inside! 2, Gregor Henze1,3,4 & Soumik Sarkar 2 records available for one home biases logging PyTorch... Fatigue behavior and visual movement behavior and light outperformed all the others, with days. Ground-Truth occupancy was occupancy detection dataset from time stamped pictures that were taken every...., different photographic distances public was chosen so as to maximize the amount of available data continuous... Scripts to reproduce exploratory figures anonymized and images were done in Python with scikit-learn33 version 0.24.1, recall...