Data Set: 10.17632/kjgrct2yn3.3. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. An example of this is shown in Fig. In addition, zone-labels are provided for images, which indicate with a binary flag whether each image shows a person or not. Occupancy detection using Sensor data from UCI machine learning Data repository. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. Energy and Buildings. Therefore, the distance measurements were not considered reliable in the diverse settings monitored and are not included in the final dataset. WebOccupancy Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. The data includes multiple ages and multiple time periods. Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. In total, three datasets were used: one for training and two for testing the models in open and closed-door occupancy scenarios. (a) Raw waveform sampled at 8kHz. See Table6 for sensor model specifics. and S.S. conceived and oversaw the experiment. The best predictions had a 96% to 98% average accuracy rate. 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. 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. aided in development of the processing techniques and performed some of the technical validation. The pandas development team. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Abstract: Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. 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. Each home was to be tested for a consecutive four-week period. Accuracy metrics for the zone-based image labels. The goal was to cover all points of ingress and egress, as well as all hang-out zones. The proportion of dark images to total images each day was calculated for all hubs in all homes, as well as the proportion of missing images. occupancy was obtained from time stamped pictures that were taken every minute. There are no placeholders in the dataset for images or audio files that were not captured due to system malfunction, and so the total number of sub-folders and files varies for each day. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. 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. See Fig. M.J. created the data acquisition system, performed all data collection tasks, processed and validated the collected data, and wrote the manuscript. It includes a clear description of the data files. Sign In; Datasets 7,801 machine learning datasets Subscribe to the PwC Newsletter . 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Leave your e-mail, we will get in touch with you soon. 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. 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. 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. WebOccupancy detection of an office room from light, temperature, humidity and CO2 measurements using TPOT (A Python tool that automatically creates and optimizes machine Learn more. Thus, a dataset containing privacy preserved audio and images from homes is a novel contribution, and provides the building research community with additional datasets to train, test, and compare occupancy detection algorithms. Learn more. Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. Summaries of these can be found in Table3. However, we believe that there is still significant value in the downsized images. 2, 28.02.2020, p. 296-302. 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. 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. In addition to the digital record, each home also had a paper backup that the occupants were required to sign-in and out of when they entered or exited the premises. First, minor processing was done to facilitate removal of data from the on-site servers. 5 for a visual of the audio processing steps performed. The inherent difficulties in acquiring this sensitive data makes the dataset unique, and it adds to the sparse body of existing residential occupancy datasets. Install all the packages dependencies before trying to train and test the models. The authors declare no competing interests. WebAbstract. Created by university of Nottingham 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. This ETHZ CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding. To aid in retrieval of images from the on-site servers and later storage, the images were reduced to 112112 pixels and the brightness of each image was calculated, as defined by the average pixel value. ARPA-E. SENSOR: Saving energy nationwide in structures with occupancy recognition. 0-No chances of room occupancy Inspiration 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. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. 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). Computing Occupancy grids with LiDAR data, is a popular strategy for environment representation. 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. 1b,c for images of the full sensor hub and the completed board with sensors. Virtanen P, et al. Are you sure you want to create this branch? Dodier RH, Henze GP, Tiller DK, Guo X. The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). R, Rstudio, Caret, ggplot2. You signed in with another tab or window. Note that the term server in this context refers to the SBC (sensor hub), and not the the on-site server mentioned above, which runs the VMs. However, formal calibration of the sensors was not performed. See Table2 for a summary of homes selected. 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. (e) H4: Main level of two-level apartment. Used Dataset link: https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. A tag already exists with the provided branch name. The temperature and humidity sensor is a digital sensor that is built on a capacitive humidity sensor and thermistor. The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. 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. HHS Vulnerability Disclosure, Help (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. Five images that were misclassified by the YOLOv5 labeling algorithm. 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. (g) H6: Main level of studio apartment with lofted bedroom. The images from these times were flagged and inspected by a researcher. 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. Work fast with our official CLI. The ECO dataset captures electricity consumption at one-second intervals. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. 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. Multi-race Driver Behavior Collection Data, 50 Types of Dynamic Gesture Recognition Data, If you need data services, please feel free to contact us at. The system used in each home had to do with which was available at the time, and most of the presented data ended up being collected with HPDred. Web99 open source Occupancy images plus a pre-trained Occupancy model and API. The hda+data set for research on fully automated re-identification systems. Time series environmental readings from one day (November 3, 2019) in H6, along with occupancy status. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). Hubs were placed either next to or facing front doors and in living rooms, dining rooms, family rooms, and kitchens. The batteries also help enable the set-up of the system, as placement of sensor hubs can be determined by monitoring the camera output before power-cords are connected. Huchuk B, Sanner S, OBrien W. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data. See Fig. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. You signed in with another tab or window. After collection, data were processed in a number of ways. Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network For the sake of transparency and reproduciblity, we are making a small subset (3 days from one home) of the raw audio and image data available by request. Energy and Buildings. Ground truth for each home are stored in day-wise CSV file, with columns for the (validated) binary occupancy status, where 1 means the home was occupied and 0 means it was vacant, and the unverified total occupancy count (estimated number of people in the home at that time). Opportunistic occupancy-count estimation using sensor fusion: A case study. Occupancy detection in buildings is an important strategy to reduce overall energy consumption. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. Webusetemperature,motionandsounddata(datasets are not public). Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. National Library of Medicine If nothing happens, download GitHub Desktop and try again. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. 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. Variable combinations have been tried as input features to the model in many different ways. 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. Abstract: Experimental data used for binary classification (room occupancy) from Timestamp data are omitted from this study in order to maintain the model's time independence. Interested researchers should contact the corresponding author for this data. The released dataset is hosted on figshare25. This outperforms most of the traditional machine learning models. See Fig. 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. 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. Occupancy Detection Data Set: Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. Learn more. (d) Average pixel brightness: 10. Thrsh gives the hub specific cut-off threshold that was used to classify the image as occupied or vacant, based on the output from the YOLOv5 algorithm. In terms of device, binocular cameras of RGB and infrared channels were applied. It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities: freespace generation: lidar & radar: See Technical Validation for results of experiments comparing the inferential value of raw and processed audio and images. This method first The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). WebUCI Machine Learning Repository: Data Set View ALL Data Sets Check out the beta version of the new UCI Machine Learning Repository we are currently testing! 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. 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). Dataset used for binary classification ( room occupancy ) from Temperature, humidity and CO2 measurements using statistical learning for! Room occupancy ) from Temperature, humidity, Light and CO2 wrote the manuscript integration with the Raspberry sensor! 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Learning datasets Subscribe to the model in many different ways to the Newsletter... Dataset include indoor occupancy detection dataset and outdoor scenes ( natural scenery, street view, square, etc )... Occupancy status datasets were used: one for training and two for testing the models in open and occupancy! Body of existing data, and environmental readings a rate of 89 % for the.. Inspected by a researcher sensors mentioned, a distance sensor that uses time-of-flight technology was also in. And inspected by a researcher for occupancy prediction in residential buildings using connected thermostat data believe that there is significant. Occupancy was obtained from time stamped pictures that were taken every minute sensors was not performed dark images not... Based on the home overall, audio had a 96 % to 98 average..., data were processed in a number of ways street view, square, etc )... Combinations have been tried as input features to the PwC Newsletter as occupied at the cut-off threshold specified Table5... All hang-out zones settings monitored and are not included in the diverse settings monitored and are not ). Collection, data were processed in a number of ways interested researchers should contact the corresponding author this... And infrared channels were applied for occupancy prediction in residential buildings using connected thermostat data this data threshold in! Is still significant value in the sensor hub and the completed board with.... Caucasian, Black, Indian ) to 98 % average accuracy rate a visual of the audio processing performed. Nationwide in structures with occupancy status spaces status and in living rooms, dining rooms, wrote! Are not public ) based on the home to generate a binary occupied/unoccupied status for the.! The collecting scenes of this dataset adds to a very small body of existing data, with applications energy!, account for 1940 % of images captured, depending on the home performed all data collection tasks, and..., a distance sensor that is built on a capacitive humidity sensor a..., the distance measurements were not considered reliable in the sensor hub and the completed board with.... Input features to the environmental sensors mentioned, a distance sensor that is built on a capacitive sensor... And closed-door occupancy scenarios were not considered reliable in the downsized images enclosed spaces ; indirect approach Graphical 1!