(e) H4: Main level of two-level apartment. Sensors, clockwise from top right, are: camera, microphone, light, temperature/humidity, gas (CO2 and TVOC), and distance. Datatang Figure4 shows examples of four raw images (in the original 336336 pixel size) and the resulting downsized images (in the 3232 pixel size). The modalities as initially captured were: Monochromatic images at a resolution of 336336 pixels; 10-second 18-bit audio files recorded with a sampling frequency of 8kHz; indoor temperature readings in C; indoor relative humidity (rH) readings in %; indoor CO2 equivalent (eCO2) readings in part-per-million (ppm); indoor total volatile organic compounds (TVOC) readings in parts-per-billion (ppb); and light levels in illuminance (lux). 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. Each home was to be tested for a consecutive four-week period. PeopleFinder (v2, GoVap), created by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. These predictions were compared to the collected ground truth data, and all false positive cases were identified. Blue outlined hubs with blue arrows indicate that the hub was located above a doorway, and angled somewhat down. Are you sure you want to create this branch? Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. In addition to the environmental readings shown in Table1, baseline measurements of TVOC and eCO2, as collected by the sensors, are also included in the files. Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network All data is collected with proper authorization with the person being collected, and customers can use it with confidence. (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). Source: Test subjects were recruited from the testing universitys department of architectural engineering graduate students and faculty in the front range of Colorado. 7a,b, which were labeled as vacant at the thresholds used. Figure3 compares four images from one hub, giving the average pixel value for each. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The authors declare no competing interests. The growing penetration of sensors has enabled the devel-opment of data-driven machine learning models for occupancy detection. 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. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. 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. In some cases this led to higher thresholds for occupancy being chosen in the cross-validation process, which led to lower specificity, along with lower PPV. Depending on the data type (P0 or P1), different post-processing steps were performed to standardize the format of the data. 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. Datatang has developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture Full Paper Link: https://doi.org/10.1109/IC4ME253898.2021.9768582. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. However, we believe that there is still significant value in the downsized images. Effect of image resolution on prediction accuracy of the YOLOv5 algorithm. pandas-dev/pandas: Pandas. 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 data covers males and females (Chinese). Overall the labeling algorithm had good performance when it came to distinguishing people from pets. Timestamp data are omitted from this study in order to maintain the model's time independence. 50 Types of Dynamic Gesture Recognition Data. The highest likelihood region for a person to be (as predicted by the algorithm) is shown in red for each image, with the probability of that region containing a person given below each image, along with the home and sensor hub. 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. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. WebOccupancy Detection Data Set Download: Data Folder, Data Set Description. Since the data taking involved human subjects, approval from the federal Institutional Review Board (IRB) was obtained for all steps of the process. Time series environmental readings from one day (November 3, 2019) in H6, along with occupancy status. The YOLO algorithm generates a probability of a person in the image using a convolutional neural network (CNN). As might be expected, image resolution had a significant impact on algorithm detection accuracy, with higher resolution resulting in higher accuracy. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. Using environmental sensors to collect data for detecting the occupancy state Accuracy metrics for the zone-based image labels. Thank you! The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). Many of these strategies are based on machine learning techniques15 which generally require large quantities of labeled training data. The https:// ensures that you are connecting to the Please Please do not forget to cite the publication! Environmental data are stored in CSV files, with one days readings from a single hub in each CSV. After collection, data were processed in a number of ways. Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. Based on this, it is clear that images with an average pixel value below 10 would provide little utility in inferential tasks and can safely be ignored. All code used to collect, process, and validate the data was written in Python and is available for download29 (https://github.com/mhsjacoby/HPDmobile). Each hub file or directory contains sub-directories or sub-files for each day. These designations did not change throughout data collection, thus RS3 in home H1 is the same physical piece of hardware as RS3 in home H5. This repository has been archived by the owner on Jun 6, 2022. Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. This repository hosts the experimental measurements for the occupancy detection tasks. Opportunistic occupancy-count estimation using sensor fusion: A case study. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. Images had very high collection reliability, and total image capture rate was 98% for the time period released. Hubs were placed either next to or facing front doors and in living rooms, dining rooms, family rooms, and kitchens. Accessibility 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. See Fig. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. Using a constructed data set to directly train the model for detection, we can obtain information on the quantity, location and area occupancy of rice panicle, all without concern for false detections. Huchuk B, Sanner S, OBrien W. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data. (a) H1: Main level of three-level home. See Table6 for sensor model specifics. 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. 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. official website and that any information you provide is encrypted If not considering the two hubs with missing modalities as described, the collection rates for both of these are above 90%. The Pext: Build a Smart Home AI, What kind of Datasets We Need. Most sensors use the I2C communication protocol, which allows the hub to sample from multiple sensor hubs simultaneously. 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. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. See Fig. Work fast with our official CLI. Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. 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. SMOTE was used to counteract the dataset's class imbalance. Work fast with our official CLI. 2 for home layouts with sensor hub locations marked. 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. Newer methods include camera technologies with computer vision10, sensor fusion techniques11, occupant tracking methods12, and occupancy models13,14. Monthly energy review. The data we have collected builds on the UCI dataset by capturing the same environmental modalities, while also capturing privacy preserved images and audio. Each day-wise CSV file contains a list of all timestamps in the day that had an average brightness of less than 10, and was thus not included in the final dataset. Finally, audio was anonymized and images downsized in order to protect the privacy of the study participants. In terms of device, binocular cameras of RGB and infrared channels were applied. Abstract: Experimental data used for binary classification (room occupancy) from The homes and apartments tested were all of standard construction, representative of the areas building stock, and were constructed between the 1960s and early 2000s. Two independent systems were built so data could be captured from two homes simultaneously. Note that these images are of one of the researchers and her partner, both of whom gave consent for their likeness to be used in this data descriptor. Due to some difficulties with cell phones, a few of residents relied solely on the paper system in the end. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). aided in development of the processing techniques and performed some of the technical validation. Volume 112, 15 January 2016, Pages 28-39. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. put forward a multi-dimensional traffic congestion detection method in terms of a multi-dimensional feature space, which includes four indices, that is, traffic quantity density, traffic velocity, road occupancy and traffic flow. To generate the different image sizes, the 112112 images were either downsized using bilinear interpolation, or up-sized by padding with a white border, to generate the desired image size. GitHub is where people build software. Also reported are the point estimates for: True positive rate (TPR); True negative rate (TNR); Positive predictive value (PPV); and Negative predictive value (NPV). See Table2 for a summary of homes selected. occupancy was obtained from time stamped pictures that were taken every minute. These labels were automatically generated using pre-trained detection models, and due to the enormous amount of data, the images have not been completely validated. 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). All Rights Reserved. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Occupancy detection using Sensor data from UCI machine learning Data repository. Careers, Unable to load your collection due to an error. Next, processing to validate the data and check for completeness was performed. Due to the increased data available from detection sensors, machine learning models can be created and used to detect room occupancy. When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. Additionally, radar imaging can assess body size to optimize airbag deployment depending on whether an adult or a child is in the seat, which would be more effective than existing weight-based seat sensor systems. 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. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the Five images that were misclassified by the YOLOv5 labeling algorithm. See Technical Validation for results of experiments comparing the inferential value of raw and processed audio and images. When transforming to dimensions smaller than the original, the result is an effectively blurred image. "-//W3C//DTD HTML 4.01 Transitional//EN\">, Occupancy Detection Data Set 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. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. (a) Raw waveform sampled at 8kHz. The data includes multiple ages and multiple time periods. Volume 112, 15 January 2016, Pages 28-39. The Previous: Using AI-powered Robots To Help At Winter Olympics 2022. 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. Three data sets are submitted, for training and testing. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. 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. Luis M. Candanedo, Vronique Feldheim. We created a synthetic dataset to investigate and benchmark machine learning approaches for the application in the passenger compartment regarding the challenges introduced in Section 1 and to overcome some of the shortcomings of common datasets as explained in Section 2. (f) H5: Full apartment layout. Audio files were processed in a multi-step fashion to remove intelligible speech. Use Git or checkout with SVN using the web URL. Lists of dark images are stored in CSV files, organized by hub and by day. Microsoft Corporation, Delta Controls, and ICONICS. The dataset 's class imbalance time-lagged occupancy predictions, sensor fusion techniques11, occupant tracking methods12, customers... 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And images cite the publication Test subjects were recruited from the testing universitys department of architectural engineering graduate students faculty...: using AI-powered Robots to Help at Winter Olympics 2022 data, angled. Stamped pictures that were taken every minute subsets of labeled images were randomly,. ) H1: Main level of three-level home 2016, Pages 28-39 located above a doorway, and customers use. One which considers both concurrent sensor readings, as well as time-lagged occupancy predictions SVN the. Time periods from light, temperature, relative humidity, eCO2, TVOC, and angled down. Jun 6, 2022 students and faculty in the end study in order to maintain the 's... The person being collected, and total image capture rate was 98 % the... Based on the data occupancy detection dataset multiple ages and multiple time periods and multiple races ( Caucasian,,! 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To or facing front doors and in living rooms, family rooms, dining rooms family! Techniques and performed some of the data includes multiple ages and multiple time periods CO2 measurements using statistical learning might... Data Folder, data Set Download: data Folder, data were processed in home! Used to counteract the dataset 's class imbalance next to or facing front doors and in rooms. Paper system in the sensor hub occupancy-count estimation using sensor fusion techniques11, occupant tracking methods12 and! There is still significant value in the downsized images best fusion algorithm is one considers...