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3 results for “mobile recognition”
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Recognition and Management of Early Mobilization in ICUs : Practice Patterns in China
ClinicalTrials.gov study NCT02804516. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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>
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