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1,356 results for “Human Activities”
Lipid-accumulated reactive astrocytes promote seizure activity in epilepsy [Human bulk RNA-seq]
GEO Series GSE190451. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities
<p>Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities</p> <p><strong>Note</strong>: The dataset is licensed and shared under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/. If you want to refer to this dataset in a publication, please use the reference below: Author names: removed for anonymous review. 2024. Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities Sci. Data 00, 000.</p> <p>1 HAR-PMD Dataset</p> <p>HAR-PMD(Human Activity Recognition for Pedestrians with Mobility Disabilities) dataset consists of smartphone and smartwatch sensor data from six pedestrian activities for people with mobility difficulties: still, walking, crutches, walker, manual wheelchairs, and electric wheelchairs. Sixty participants collected smartphone data, and sixty additional participants collected both smartphone and smartwatch data. Each activity was conducted in both indoor and outdoor environments. Thirteen smartphone sensors and five smartwatch sensors were collected. As a result, the dataset consists of 14,400 minutes of data from 120 participants.</p> <p>For detailed descriptions of activities, environments, sensors, positions, data collection application, and data collection procedure, see METHODS in the paper.</p> <p> </p> <p>2 DATA FORMAT</p> <p>2.1 HAR-PMD Dataset</p> <p>The dataset consists of 120 folders for each individual. Each folder contains metadata, smartphone, and smartwatch data if collected. Smartphone and smartwatch data are stored in comma-separated values (CSV) format files.</p> <p>2.1.1 Metadata</p> <ul> <li><user_id>/device.txt – smartphone model of the participant</li> <li><user_id>/os_version.txt – Android OS version of the smartphone</li> </ul> <p>2.1.2 Smartphone</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartphone sensor data <ul> <li><user_id>: 1 – 120</li> <li><device>: phone</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.1.3 Smartwatch</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartwatch sensor data <ul> <li><user_id>: 61 – 120</li> <li><device>: watch</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.2 Data Files</p> <p>2.2.1 Smartphone sensor data</p> <p>Table 1 describes each column in a smartphone sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 1: Smartphone sensor data description for each column></p> <table> <tbody> <tr> <th>Columns</th> <th>Description (unit)</th> </tr> <tr> <td>Time</td> <td>Timestamp (s)</td> </tr> <tr> <td>LAccX</td> <td>Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr> <td>LAccY</td> <td>Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr> <td>LAccZ</td> <td>Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr> <td>GyrX</td> <td>Rate of rotation around the x axis (rad/s)</td> </tr> <tr> <td>GyrY</td> <td>Rate of rotation around the y axis (rad/s)</td> </tr> <tr> <td>GyrZ</td> <td>Rate of rotation around the z axis (rad/s)</td> </tr> <tr> <td>MagX</td> <td>Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr> <td>MagY</td> <td>Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr> <td>MagZ</td> <td>Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr> <td>GraX</td> <td>Force of gravity in the x-axis (m/s²)</td> </tr> <tr> <td>GraY</td> <td>Force of gravity in the y-axis (m/s²)</td> </tr> <tr> <td>GraZ</td> <td>Force of gravity in the z-axis (m/s²)</td> </tr> <tr> <td>AccX</td> <td>Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr> <td>AccY</td> <td>Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr> <td>AccZ</td> <td>Acceleration force including gravity along the z-axis (m/s²)</td> </tr> <tr> <td>Ori_Azimuth</td> <td>Angle around the x-axis (rad)</td> </tr> <tr> <td>Ori_Pitch</td> <td>Angle around the y-axis (rad)</td> </tr> <tr> <td>Ori_Roll</td> <td>Angle around the z-axis (rad)</td> </tr> <tr> <td>RotVec_0</td> <td>Rotation vector component along the x-axis (unitless)</td> </tr> <tr> <td>RotVec_1</td> <td>Rotation vector component along the y-axis (unitless)</td> </tr> <tr> <td>RotVec_2</td> <td>Rotation vector component along the z-axis (unitless)</td> </tr> <tr> <td>RotVec_3</td> <td>Scalar component of the rotation vector (unitless)</td> </tr> <tr> <td>Game_RotVec_0</td> <td>Rotation vector without using geomagnetic filed component along the x-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_1</td> <td>Rotation vector without using geomagnetic filed component along the y-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_2</td> <td>Rotation vector without using geomagnetic filed component along the z-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_3</td> <td>Scalar component without using geomagnetic filed of the rotation vector (unitless)</td> </tr> <tr> <td>Pressure</td> <td>Ambient air pressure (hPa)</td> </tr> <tr> <td>Height</td> <td>Altitude (m)</td> </tr> <tr> <td>Light</td> <td>Illuminance (lx)</td> </tr> <tr> <td>Step</td> <td>Number of steps (steps)</td> </tr> <tr> <td>Proxi</td> <td>Proximity (cm)</td> </tr> </tbody> </table> <p> </p> <p>2.2.2 Smartwatch sensor data</p> <p>Table 2 describes each column in a smartwatch sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 2: Smartwatch sensor data description for each column></p> <table style="width: 42.344%; height: 382.282px;"> <tbody> <tr style="height: 19.5938px;"> <th style="width: 16.3311%; height: 19.5938px;">Columns</th> <th style="width: 83.6689%; height: 19.5938px;">Description (unit)</th> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">Time</td> <td style="width: 83.6689%; height: 19.5938px;">Timestamp (s)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccX</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccY</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccZ</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrX</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the x axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrY</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the y axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrZ</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the z axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagX</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagY</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagZ</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraX</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the x-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraY</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraZ</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccX</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 10px;"> <td style="width: 16.3311%; height: 10px;">AccY</td> <td style="width: 83.6689%; height: 10px;">Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccZ</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the z-axis (m/s²)</td> </tr> </tbody> </table> <p> </p> <p><span lang="EN-US">3. Benchmark Experiment</span></p> <p><span lang="EN-US">We applied four machine learning models (Decision Tree, Random Forest, XGBoost, and Support Vector Machine) and four deep learning models (Multilayer Perceptron, Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Transformer) to conduct the benchmark experiments. </span></p> <p><span lang="EN-US">We had three validation scenarios: a) Mobility disability; b) Walking aids & wheelchairs; and c) Mobility in detail. Data from all participants and activities were used for each validation scenarios. Each scenario was evaluated using two validation methods: user-depedent (random) evaluation and user-independent evaluation. In the user-dependent (random) evaluation, we randomly shuffled the data and divided the 14,400 min of data into five subsets of 2,880 min each for smartphone sensor data. After shuffling and splitting the data, one subset was allocated for testing while the other four subsets were used for training the model, and this process was repeated for all five subsets. For the combination of smartphone and smartwatch sensors, 7,200 min of data was randomly shuffled and divided the five subsets of 1,440 min data. Each subset was again allocated for testing while the other four subsets were used for training. In the user-independent (UI) evaluation, we used leave-one-group-out 5-fold cross-validation. For smartphone sensor data, 120 participants who comprised the data were divided into five equally distributed subsets of 24 participants each. The models were trained using the data of 96 participants and tested one of the data for the remaining 24 participants. For the combination of smartphone and smartwatch sensors, as the smartwatch data consisted of 60 participants, the data from 48 participants were used for training and the remaining 12 participants were used for testing. This list of participants included in each fold is shown in the below table. In the combination of sensor, we used three sensor combinations: a) linear accelerometer; b) linear accelerometer and gyroscope, and c) linear accelerometer, gyroscope, and magnetometer. Data from all participants and activities were used for each sensor scenarios.</span></p> <p> </p> <table style="width: 67.8639%; height: 136.172px;"> <tbody> <tr style="height: 33.6094px;"> <th style="width: 16.4575%; height: 33.6094px;"> </th> <th style="width: 17.0153%; height: 33.6094px;">Fold 1</th> <th style="width: 16.8759%; height: 33.6094px;">Fold 2</th> <th style="width: 16.5962%; height: 33.6094px;">Fold 3</th> <th style="width: 16.3188%; height: 33.6094px;">Fold 4</th> <th style="width: 16.7364%; height: 33.6094px;">Fold 5</th> </tr> <tr style="height: 47.1875px;"> <td style="width: 16.4575%; height: 47.1875px;">Smartphone only</td> <td style="width: 17.0153%; height: 47.1875px;">1-24</td> <td style="width: 16.8759%; height: 47.1875px;">25-48</td> <td style="width: 16.5962%; height: 47.1875px;">49-72</td> <td style="width: 16.3188%; height: 47.1875px;">73-96</td> <td style="width: 16.7364%; height: 47.1875px;">96-120</td> </tr> <tr style="height: 55.375px;"> <td style="width: 16.4575%; height: 55.375px;">Smartphone & smartwatch</td> <td style="width: 17.0153%; height: 55.375px;">61-72</td> <td style="width: 16.8759%; height: 55.375px;">73-84</td> <td style="width: 16.5962%; height: 55.375px;">85-96</td> <td style="width: 16.3188%; height: 55.375px;">97-108</td> <td style="width: 16.7364%; height: 55.375px;">109-120</td> </tr> </tbody> </table> <p> </p>
Human Activity and Environmental Metrics for Anomaly Detection
<p>This dataset contains records of human physiological and environmental data, all collected in a consistent environment at the same time intervals, designed to support research in anomaly or attack detection. The measurements simulate conditions under which physiological parameters may fluctuate, potentially indicating anomalous activities or conditions.</p> <p>The dataset includes 4002 entries with the following parameters:</p> <ul> <li>Humidity: Environmental humidity levels measured in percentage.</li> <li>Temperature: Environmental temperature recorded in degrees.</li> <li>Step count: The number of steps taken by an individual.</li> <li>motion_values: A measurement representing physical motion intensity.</li> <li>heart_rate: Heart rate measured in beats per minute.</li> <li>Attack: Binary indicator (1 or 0) denoting an "attack" or anomalous state (1) versus normal state (0).</li> </ul> <p> </p>
Maps for Human-scale Accessibility in London Central Activity Zone
<p>This dataset is the result of individual research aimed at developing a new metric, Human-Scale Accessibility (HSA), to assess the level of 15-minute cities locally. The basic unit of the dataset is streets, with HSA values calculated for all streets, along with the contributing variables.</p>
Fig. 1 in Investigation of Dalea parryi (Fabaceae) metabolites for anthelmintic activity against the human pathogenic hookworm Ancylostoma ceylanicum
Fig. 1. Structures of compounds 1–13 isolated from Dalea parryi.
Changes in the Permeability of the Blood Brain Barrier and Intestinal Barrier in Humans During Conditions of Stress and Immune Activation: in Vivo Studies
ClinicalTrials.gov study NCT01417416. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Early Access Program of the Safety of Human Anti-TNF Monoclonal Antibody Adalimumab in Subjects With Active Rheumatoid Arthritis
ClinicalTrials.gov study NCT00650026. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of the Proliferative Activities of Insulin Analogues in Primary Human Tumor Cells
ClinicalTrials.gov study NCT01267461. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Intermittent and sustained hypoxia exposures activate distinct transcriptional responses in human aortic endothelial cells
GEO Series GSE279434. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.
GEO Series GSE156735. Homo sapiens. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Generating IGF1 signatures in human melanoma cell lines by activation of IGF1 or ihibition of IGF1R
GEO Series GSE59343. Homo sapiens. 24 samples. Type: Expression profiling by array.
Integration activity of evolutionarily recent human retroelements Alu and LINE in tumors
GEO Series GSE288482. Homo sapiens. 52 samples. Type: Expression profiling by high throughput sequencing.
TOP2B binding and enzymatic activity on promoters and introns modulates multiple oncogenes in human gliomas
GEO Series GSE133561. Homo sapiens. 75 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
ORC1 binds to cis-transcribed RNAs for efficient activation of human origins [ORC1_iCLIP]
GEO Series GSE173449. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing; Other.
ORC1 binds to cis-transcribed RNAs for efficient activation of human origins [ORC1_RIPseq]
GEO Series GSE173438. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Recombinant human lactoferrin activates human dendritic cells via Toll-like receptors-2 and -4
GEO Series GSE26438. Homo sapiens. 12 samples. Type: Expression profiling by array.
Spontaneous functional network activity in organoids resembles programmed early human brain development
GEO Series GSE113089. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
RNA-Binding Activity of PHGDH Drives Amyloid-beta Production in A Human Brain Organoid Model of Sporadic Alzheimer’s Disease
GEO Series GSE302314. Homo sapiens. 8 samples. Type: Other.
Human cytomegalovirus infection coopts chromatin organization to modulate TEAD1 transcription factor activity [HiChIP]
GEO Series GSE254737. Homo sapiens. 6 samples. Type: Other.
Human cytomegalovirus infection coopts chromatin organization to modulate TEAD1 transcription factor activity [RNA-Seq]
GEO Series GSE254735. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
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OpenNeuro
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