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342 results for “daily activity”
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
Synthetic Multimodal Dataset for Daily Life Activities
<p><strong>Outline</strong></p> <ul> <li>This dataset is originally created for the <a href="https://challenge.knowledge-graph.jp/2022/">Knowledge Graph Reasoning Challenge for Social Issue</a>s (KGRC4SI)</li> <li>Video data that simulates daily life actions in a virtual space from Scenario Data.</li> <li>Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).</li> <li>Knowledge Graph Embedding Data are created for reasoning based on machine learning </li> <li>This data is open to the public as open data</li> </ul> <p><strong>Details</strong></p> <ul> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Movie">Videos</a></p> <ul> <li>mp4 format</li> <li>203 action scenarios</li> <li>For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos</li> <li>Videos with slowly moving characters simulate the movements of elderly people.</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/RDF">Knowledge Graphs</a></p> <ul> <li>RDF format</li> <li>203 knowledge graphs corresponding to the videos</li> <li>Includes schema and location supplement information</li> <li>The schema is described below</li> <li><a href="http://kgrc4si.ml:7200/sparql">SPARQL endpoints</a> and <a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/tree/kgrc4si#%E3%83%8A%E3%83%AC%E3%83%83%E3%82%B8%E3%82%B0%E3%83%A9%E3%83%95%E3%81%AE%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95">query examples</a> are available</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Program">Script Data</a></p> <ul> <li>txt format</li> <li>Data provided to VirtualHome2KG to generate videos and knowledge graphs</li> <li>Includes the action title and a brief description in text format.</li> </ul> </li> <li>Embedding <ul> <li>Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (<a href="https://dglke.dgl.ai/doc/">https://dglke.dgl.ai/doc/</a>)</li> <li>Embedding Vectors created with jRDF2vec (<a href="https://github.com/dwslab/jRDF2Vec">https://github.com/dwslab/jRDF2Vec</a>).</li> </ul> </li> </ul> <p><strong>Specification of Ontology</strong></p> <ul> <li>Please refer to the specification for descriptions of all classes, instances, and properties: <a href="https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.html">https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm</a></li> </ul> <p><strong>Related Resources</strong></p> <ul> <li><a href="https://www.youtube.com/watch?v=Ajbn8hNXiZ8&list=PLHaRK-B0LUwjvrPgmIBTrf3DsPhmdnFTW">KGRC4SI Final Presentations with automatic English subtitles (YouTube)</a></li> <li><a href="https://github.com/aistairc/VirtualHome2KG">VirtualHome2KG (Software)</a></li> <li><a href="https://github.com/aistairc/virtualhome_unity_aist">VirtualHome-AIST (Unity</a>)</li> <li><a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST (Python API</a>)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_visualization">Visualization Tool</a> (Software)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_generation">Script Editor</a> (Software)</li> </ul>
Daily summaries of photosynthetically active radiation (PAR), relative humidity, and temperature data logged above, within, and below Betula nana and Salix pulchra shrub canopies during the summer of 2012 in vicinity of Toolik Lake, Alaska.
This file contains limited daily summaries of PAR, relative humidity, and temperature data monitored above, within, and below Betula nana and Salix pulchra shrub canopies at two locations near Toolik Lake, Alaska during the summer of 2012. The location of the PAR sensor and dataloggers were co-located with the LTER shrub plots (block 1 and 2), also used for the chamber flux and point frame measurements taken this same year. There were two logging sites (block 1 and 2), each of which had PAR five PAR sensors, two for each shrub canopy and one above, as well as three sensors to log relative humidity and temperature. This file contains maximum PAR, total daily PAR, and daily average temperature data, as well as intermittant maximum and minimum values for temperature and relative humidity. Data monitored every five minutes is available in the file "ShrubCanopy_InstantLogger".
Daily aerosol emissions changes in 2020 due to Covid19: modified SSP2-4.5 to account for sector activity level
<p>Daily aerosol emissions estimates for 2020, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for aerosol and precursor emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020, with observation-based data up until the 5th of July and a fixed estimate thereafter. This is the daily equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a>. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Daily Activity and Nest Occupation Patterns of Fox Squirrels (Sciurus niger) Throughout the Year
<p>The daily distribution of activity has been studied in detail in ground squirrels in the field as well as in the laboratory, but studies of tree squirrels have been few and generally limited to the sampling of behavior of groups of animals. In this study, the authors investigated the general activity and nest occupation patterns of fox squirrels in a natural setting using temperature-sensitive data loggers that measure activity as changes in the microenvironment of the animal. Data were obtained from 25 distinct preparations, upon 13 unique squirrels, totaling 1385 recording days. Fox squirrels exhibited robust daily rhythmicity of locomotor activity, comparable to that of laboratory rats and gerbils. The animals were clearly diurnal, with a predominantly unimodal activity pattern, although individual squirrels occasionally exhibited bimodal patterns, particularly in the spring and summer. Even during the short days of winter (9 hours), the squirrels typically left the nest after dawn and returned before dusk, spending only about 7 hours out of the nest each day. Although the duration of the daily active phase did not change with the seasons, the squirrels exited the nest earlier in the day when the days became longer in the summer and exited the nest later in the day when the days became shorter in the winter, thus tracking dawn along the seasons. During the few hours each day spent outside the nest, fox squirrels seemed to spend most of the time sitting or lying. These findings suggest that fox squirrels may have adopted a slow life history strategy.</p>
ExoMove - Kinematics of Daily Activities with Lower-Limb Exoskeletons
<p>This dataset reports the lower-limb kinematics of healthy individuals during various daily activities (sitting, walking, stair ascending and descending, and transitions between them) while using two distinct lower-limb exoskeletons, eWalk and Autonomyo.<br><br>The dataset captures the biomechanical differences between the exoskeletons, offering a rich resource for advancing exoskeleton design and control for assistive and rehabilitative applications.</p>
Daily Activity Schedule Tel Aviv 2040 from SimMobility MIT Preday
<p>This database countain the activities conducted in the Tel Aviv metropolis on a typical day in 2040. The information is derived from the outcomes of the Simobility demand simulator, which operates on synthetic population and land-use inputs specific to the Tel Aviv metropolis predictions.</p><p> </p>
Daily Activity Schedule Tel Aviv 2017 from SimMobility MIT Preday
<p>This database countain the activities conducted in the Tel Aviv metropolis on a typical day in 2017. The information is derived from the outcomes of the Simobility demand simulator, which operates on synthetic population and land-use inputs specific to the Tel Aviv metropolis.</p>
Daily Life Activities Dataset
<p><strong>Experiment design</strong></p> <p>The Daily Life Activities (DLA) dataset consists of trials of daily life object manipulation tasks performed by a human. The dataset consists of ten tasks: <em>cutting, painting, pouring with cup, putting cup away, quarter turn, scooping and pouring, scooping food, shaking, sinusoidal motion</em>, and <em>table wiping</em>. In this dataset, a high variation in the context was purposefully introduced. That is, the tasks were performed with respect to three different viewpoints (V1, V2, V3) and with four different execution styles (normal, with larger spatial scale, with different velocity profile, and with longer time duration). This resulted in a total of (3x4=12) twelve different contexts in which the tasks were performed. Each task was performed ten times in every context, resulting in a total of (10x3x4x10=1200) trials.</p> <p><strong>Experimental setup</strong></p> <p>The trials were recorded using a Krypton K600 camera from NIKON Metrology by tracking up to nine LED markers attached to the manipulated object. The 3D position of each LED marker was recorded with a sampling rate of 50 Hz and expected accuracy of 0.4mm with respect to the measurement frame of the camera system.</p> <p><strong>Data format</strong></p> <p>Every trial_xxx.mat file is a Matlab structure array. The trailing number xxx refers to the order in which the trials were performed. For every task:</p> <ul> <li> <em>trial_001.mat</em> up to <em>trial_040.mat </em>were recorded in sensor viewpoint 1. <ul> <li><em>trial_001.mat</em> up to <em>trial_010.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_011.mat</em> up to <em>trial_020.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_021.mat</em> up to <em>trial_030.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_031.mat</em> up to <em>trial_040.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li> <em>trial_041.mat</em> up to <em>trial_080.mat </em>were recorded in sensor viewpoint 2. <ul> <li><em>trial_041.mat</em> up to <em>trial_050.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_051.mat</em> up to <em>trial_060.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_061.mat</em> up to <em>trial_070.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_071.mat</em> up to <em>trial_080.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li><em> trial_081.mat</em> up to <em>trial_120.mat </em>were recorded in sensor viewpoint 3. <ul> <li><em>trial_081.mat</em> up to <em>trial_090.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_091.mat</em> up to <em>trial_100.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_101.mat</em> up to <em>trial_110.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_111.mat</em> up to <em>trial_120.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> </ul> <p>The structure array trial_xxx.mat has the following fields:</p> <ul> <li>'number_of_timesamples': the total number of timesamples (N) for the recorded task,</li> <li>'K6C_12250_3_x': a 4xN matrix containing the 3D position coordinates of the LED marker expressed in millimeters. The trailing number x in 'K6C_12250_3_x' refers to the LED number, which can range from 1 to 9. <ul> <li>In case the LED marker was visible, the first, second and third row contain the x-, y-, and z-coordinates of the marker, respectively. The fourth row contains the zero value in this case.</li> <li>In case the LED marker was not visible, the first, second and third row contain zero values. The fourth row contains a non-zero value in this case.</li> </ul> </li> </ul>
Fig. 4 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 4. Overlap of daily activities between the pairs of the four felid species found in the Papagaios-de-Altitude Private Protected Area in Urupema, Santa Catarina, southern Brazil. The area colored in gray indicates the overlapping of daily activity in each species pair. The parallel lines in each graph indicate the mean time of sunrise (yellow) and sunset (blue) in the study region. Below each graph is the coefficient of overlap for each pair of species, and the confidence interval.
Fig. 3 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 3. Circular histograms with distribution and frequency of the seasonal activity of Leopardus guttulus, L. pardalis, L. wiedii and Puma concolor in Papagaios-de-Altitude Private Protected Area, Santa Catarina, Brazil. The arrows on each graph indicate the direction of the mean angle (µ). Each graph also exhibits the values of P and mean vector length (r).
Fig. 2 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 2. Circular histograms with distribution and frequency of daily activity of Leopardus guttulus, L. pardalis, L. wiedii and Puma concolor in Papagaiosde-Altitude Private Protected Area, Santa Catarina, Brazil. The black arrows on each graph indicate the direction of the mean angle (µ). Each graph also exhibits the values of P and mean vector length (r).
Fig. 1 in Daily and seasonal activity patterns of a felid assemblage in a forest-grassland mosaic in southern Brazil
Fig. 1. Study area: Papagaios-de-Altitude Private Protected Area in Urupema, Santa Catarina, Brazil. On the top-right panel, colors indicate the type of soil-use in the area and its limits. The black dots in the map indicate the location of the camera traps.
Fig. 3 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 3. Temporal overlap of Leopardus guttulus (Hensel, 1872) activity during the different seasons: autumn/winter and spring/summer; the gray area represents the overlap between the activity observed in the two periods of the year and the vertical lines represent sunrise and sunset in each period (autumn/winter: 06h 45min sunrise and 18h 05min sunset; spring/summer: 06h 08min sunrise and 19h 33min sunset).
Fig. 4 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 4. Circular graph showing the distribution of Leopardus guttulus (Hensel, 1872) records in BRLJL, Rio Grande do Sul, Brazil, throughout the 12 months sampled. The black lines represent the concentration of records.
Fig. 2 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 2. Circular graphs showing the daily activity of Leopardus guttulus (Hensel, 1872) at the BRLJL, Rio Grande do Sul, Brazil, based on all records obtained for the species (n=25, all seasons), on records obtained during spring/summer (n=15), and on records from autumn/winter (n=10). The arrow on each circular graphs indicates the direction of the angular mean.
Fig. 1 in Daily activity patterns and occurrence of Leopardus guttulus (Carnivora, Felidae) in Lami Biological Reserve, southern Brazil
Fig. 1. Location of the study area in South America and the State of Rio Grande do Sul (left panel), with the indication of the geographic range of Leopardus guttulus (Hensel, 1872) (grey area) in Brazil, Paraguay and Argentina (above), and the Biological Reserve Lami JosÉ Lutzenberger – BRLJL in the municipality of Porto Alegre, Rio Grande do Sul, Brazil (below). On the right panel, a detailed map of the study area showing the limits of the BRLJL (black line), the vegetation types occurring in the area, the grid sQuares of 1 x 1 km (grey lines) designed to delimitate the zones where sampling stations (camera stations) were installed (black triangles).
Physiological signals during activities for daily life: Dataset
<p>The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I.</p> <p> </p> <table align="center"> <thead> <tr> <th scope="col"> </th> <th scope="col">Participant 1</th> <th scope="col">Participant 2</th> <th scope="col">Participant 3</th> <th scope="col">Participant 4</th> </tr> </thead> <tbody> <tr> <td>Age</td> <td>67</td> <td>55</td> <td>60</td> <td>63</td> </tr> <tr> <td>Gender</td> <td>Male</td> <td>Female</td> <td>Male</td> <td>Female</td> </tr> <tr> <td> <p>Final Valid Days</p> </td> <td>9</td> <td>15</td> <td>12</td> <td>13</td> </tr> <tr> <td>Total EMAs</td> <td>42</td> <td>57</td> <td>64</td> <td>46</td> </tr> </tbody> </table> <p> Table I. Summary of participants' collected data.</p> <p> </p> <p>This dataset provides three different type of labels. <em>Activeness</em> and <em>happiness</em> are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between <em>0</em> and <em>4</em>.<br> These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment.</p> <p>The data we provide in this repository consist of two type of files:</p> <ul> <li><strong>CSV files</strong>: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints. <br> </li> <li><strong>Excel files</strong>: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for <em>pleasure</em>, <em>activeness</em> and <em>mood</em> can be found in this file. </li> </ul> <p><strong>NOTE: </strong>Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files.</p> <p> </p> <p>Code and a tutorial of how to labelled and extract features can be found in this repository: <a href="https://github.com/edugm94/temporal-feat-emotion-prediction">https://github.com/edugm94/temporal-feat-emotion-prediction</a></p> <p> </p> <p>References:</p> <p>[1] . A. Russell, “A circumplex model of affect,” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980</p>
No relationship between chronotype and timing of breeding when variation in daily activity patterns across the breeding season is taken into account
<p>There is increasing evidence that individuals are consistent in the timing of their daily activities, and that individual variation in temporal behaviour is related to the timing of reproduction. However, it remains unclear whether observed patterns relate to the timing of the onset of activity or whether an early onset of activity extends the time that is available for foraging. This may then again facilitate reproduction. Furthermore, the timing of activity onset and offset may vary across the breeding season, which may complicate studying the above mentioned relationships. Here, we examined in a wild population of great tits (Parus major) whether an early clutch initiation date may be related to an early onset of activity and/or to longer active daylengths. We also investigated how these parameters are affected by the date of measurement. In order to test these hypotheses we measured emergence and entry time from/into the nest box as proxies for activity onset and offset in females during the egg laying phase. We then determined active daylength. Both emergence time and active daylength were related to clutch initiation date. However, a more detailed analysis showed that the timing of activities with respect to sunrise and sunset varied throughout the breeding season both within and among individuals. The observed positive relationships are hence potentially statistical artifacts. After methodologically correcting for this date effect, by using data from the pre-egg laying phase, where all individuals were measured on the same days, neither of the relationships remained significant. Taking methodological pitfalls and temporal variation into account may hence be crucial for understanding the significance of chronotypes.</p>
FIGURE 3 in Daily rhythm of locomotor and reproductive activity in the annual fish Garcialebias reicherti (Cyprinodontiformes: Rivulidae)
FIGURE 3 | Contextual modulation of locomotor activity of Garcialebias reicherti. Total daily locomotor activity for isolated (n = 10) and paired fish (n = 10). Each dot represents the mean number of events for each fish. *shows statistical significance (see p value in the main text). Box height from upper to lower quartile, whiskers represent standard deviation, median shown by horizontal line.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.