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Dataset results
114 results for “garments”
A Study to Determine the Safety and Efficacy of a New Non-invasive Heating Garment to Reduce Urinary Hesitancy
ClinicalTrials.gov study NCT00700505. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Wearable Garment vs Gel Electrodes for Electrical Muscle Stimulation
ClinicalTrials.gov study NCT07050784. IPD Sharing: NO. Countries: 1. Publications: 0.
Clinical Investigation of the Effects of Semiconductor Embedded Therapeutic Garments on Cancer-related Cognitive Impairment in Breast and Gynecological Cancer Patients
ClinicalTrials.gov study NCT07173101. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of Ben-Guard Garment in Reducing Pediatric Central Line Complications
ClinicalTrials.gov study NCT05109338. IPD Sharing: NO. Countries: 1. Publications: 0.
Efficacy of Lycra Garments in Ataxic Subjects
ClinicalTrials.gov study NCT04830904. IPD Sharing: NO. Countries: 1. Publications: 0.
Kinesiotaping Versus Pressure Garments on Secondary Upper Extremity Lymphedema.
ClinicalTrials.gov study NCT06230913. IPD Sharing: YES. Countries: 1. Publications: 0.
Evaluation of the Effect of Custom Compression Garments on Standing Static Balance in Ehlers Danlos Syndrome
ClinicalTrials.gov study NCT03986229. IPD Sharing: NO. Countries: 1. Publications: 0.
Use of Pressure Garments for Burn Scars
ClinicalTrials.gov study NCT00675974. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effects of Dynamic Compression Garments in Children With CP
ClinicalTrials.gov study NCT05592236. IPD Sharing: Not stated. Countries: 0. Publications: 0.
MONITOR PhysiCube Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using PhysiCube Sensors
<h3><strong>MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life</strong></h3> <p>The project, <strong>MONITOR</strong>, aims to establish mobility of bilateral collaboration between<strong> Islamic University of Technology (IUT), Bangladesh</strong>, and <strong>Mälardalen University (MDH), Sweden</strong> in specific research and education domains of Science and Technology at an institutional level. MDH has been leading several projects to develop tools to classify physiological data for individualized classification of physiological sensor readings to monitor stress, fatigue, and mental load at work and home environments. The project was funded by <strong>Stiftelsen för internationalisering av högre utbildning och forskning (STINT) </strong>under the <strong>Grant Agreement No. 202100-2916</strong>.</p> <p>This project has unfolded many benefits for both the partnering universities. It gave access to a wide variety of data regarding mental health-related parameters to the researchers. Given the data collection was done in Bangladesh, it was challenging for the researchers to extract correct and accurate data under harsh/unsafe conditions. This trained the researchers to collect data under real complex conditions. In this project, MDH provided resources and knowledge to update the already developed AI algorithms to support Garment workers’ mental health monitoring. IUT provided expert human resources to develop a sensor-setting framework and assisted in collecting and analyzing the data.</p> <p><strong>Northern Tosrifa Group (NTG):</strong> A LEED-certified garment factory in Bangladesh, provided the test environment and subjects to collect and validate the system. The group has 5 companies under its operation and all the companies manufacture and export their products to markets in North America and Europe. For the project MONITOR, <strong>Fashion Asia Limited (FAL) </strong>(one of the 5 companies under NTG) provided the necessary manpower and data collection environment for the project. </p> <h3><strong>About the Dataset</strong></h3> <p>As part of the MONITOR project, an in-house sensor device, named <strong>PhysiCube</strong>, was developed for measuring the following physiological parameters-</p> <ol> <li><strong>Object Temperature(F): </strong>The skin temperature of the subject (measured in Fahrenheit) was recorded using a low-cost off-the-shelf temperature sensor.</li> <li><strong>Heart Rate Ear(BPM): </strong>The heart rate of the subject (measured in Beats per Minute) was recorded using a heart rate sensor wearable on the ear lobe.</li> <li><strong>Galvanic Skin Response (GSR): </strong>The Galvanic Skin Response (GSR), which measures the skin’s electrical conductivity in response to sweat gland activity in the fingers and palms was measured using a low-cost off-the-shelf GSR Sensor.</li> </ol> <p>The dataset contains the timestamped values (as elapsed time, in the format '<strong>%MM:%SS</strong>') of all the aforementioned parameters for 24 subjects <strong>(29.17% Male, 70.83% Female, Age Group: 18-40 years)</strong> with written consent.</p> <p>Data were collected in the following three phases-</p> <ol> <li><strong>Baseline Values:</strong> Values of the aforementioned physiological markers that reflect the corresponding values under normal/relaxed conditions. In other words, the stress-free values of these physiological markers. Example filename: <strong>C01_PF_base.csv</strong>.</li> <li><strong>After-Lunch Values: </strong>Values that were recorded after the lunch break. Example filename: <strong>C10_PM_after_lunch.csv</strong>.</li> <li><strong>Stress-Induced Values: </strong>Values that were recorded while inducing stress using various stress-inducing questionnaires available in the literature. Example filename: <strong>C10_PM_stress_induced.csv</strong>.</li> </ol> <p>Files were organized in a way such that each subject had a directory. In each directory, files were named as '<strong><subject_id>_<device_tag><gender_tag>_<category>.csv</strong>'. For example:</p> <ol> <li><strong>Base</strong> data of a Female subject <strong>C01</strong> recorded using a <strong>PhysiCube </strong>sensor will have the name: <strong>C01_PF_base.csv</strong></li> <li><strong>After Lunch</strong> data of a Male subject <strong>C10</strong> recorded using a <strong>PhysiCube </strong>sensor will have the name: <strong>C10_PM_after_lunch.csv</strong></li> </ol> <p><strong>N.B. :</strong> As part of the <strong>MONITOR </strong>project, another <a href="../doi/10.5281/zenodo.11523282" target="_blank" rel="noopener">dataset</a> titled, "<strong>MONITOR Shimmer Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using Shimmer Sensors</strong>", was simultaneously developed using <strong>Shimmer </strong>sensors, to measure the physiological data of the same subjects.</p>
MONITOR Shimmer Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using Shimmer Sensors
<h3><strong>MONITOR: A Data-driven Intelligent MONITORring System to Improve Quality of Working Life</strong></h3> <p>The project, <strong>MONITOR</strong>, aims to establish mobility of bilateral collaboration between<strong> Islamic University of Technology (IUT), Bangladesh</strong>, and <strong>Mälardalen University (MDH), Sweden</strong> in specific research and education domains of Science and Technology at an institutional level. MDH has been leading several projects to develop tools to classify physiological data for individualized classification of physiological sensor readings to monitor stress, fatigue, and mental load at work and home environments. The project was funded by <strong>Stiftelsen för internationalisering av högre utbildning och forskning (STINT) </strong>under the <strong>Grant Agreement No. 202100-2916</strong>.</p> <p>This project has unfolded many benefits for both the partnering universities. It gave access to a wide variety of data regarding mental health-related parameters to the researchers. Given the data collection was done in Bangladesh, it was challenging for the researchers to extract correct and accurate data under harsh/unsafe conditions. This trained the researchers to collect data under real complex conditions. In this project, MDH provided resources and knowledge to update the already developed AI algorithms to support Garment workers’ mental health monitoring. IUT provided expert human resources to develop a sensor-setting framework and assisted in collecting and analyzing the data.</p> <p><strong>Northern Tosrifa Group (NTG):</strong> A LEED-certified garment factory in Bangladesh, provided the test environment and subjects to collect and validate the system. The group has 5 companies under its operation and all the companies manufacture and export their products to markets in North America and Europe. For the project MONITOR, <strong>Fashion Asia Limited (FAL) </strong>(one of the 5 companies under NTG) provided the necessary manpower and data collection environment for the project. </p> <h3><strong>About the Dataset</strong></h3> <p>As part of the MONITOR project, <strong>27</strong> subjects <strong>(29.17% Male, 83.33% Female, Age Group: 18-40 years) </strong>were recruited with written consent to collect stress-related physiological parameters. However, data from <strong>24 </strong>subjects <strong>(29.17% Male, 70.83% Female, Age Group: 18-40 years)</strong> were considered due to inconsistencies in the data from <strong>3 </strong>subjects owing to measurement inconsistencies, and device and/or human errors. <strong>Shimmer</strong> sensors were used for measuring the following physiological parameters-</p> <ol> <li><strong>Heart Rate Ear(BPM): </strong>The heart rate of the subject (measured in Beats per Minute) was recorded using the embedded heart rate sensor in the Shimmer module, wearable on the ear lobe.</li> <li><strong>Galvanic Skin Response (GSR): </strong>The Galvanic Skin Response (GSR), which measures the skin’s electrical conductivity in response to sweat gland activity in the fingers and palms, was measured using the embedded GSR Sensor in the Shimmer module.</li> </ol> <p>The dataset contains the timestamped values (as elapsed time, in the format '<strong>%MM:%SS</strong>') of all the parameters above.</p> <p>Data were collected in the following three phases-</p> <ol> <li><strong>Baseline Values:</strong> Values recorded under stress-free conditions (at the beginning of the working shift of a garment worker). Example filename: <strong>C01_SF_base.csv</strong>.</li> <li><strong>After-Lunch Values: </strong>Values recorded after the lunch break. Example filename: <strong>C10_SM_after_lunch.csv</strong>.</li> <li><strong>Stress-Induced Values: </strong>Values recorded while inducing stress using various stress-inducing questionnaires available in the literature. Example filename: <strong>C10</strong>_<strong>SM_stress_induced.csv</strong>.</li> </ol> <p>Files were organized in a way such that each subject had a directory. In each directory, files were named as '<strong><subject_id>_<device_tag><gender_tag>_<category>.csv</strong>'. For example:</p> <ol> <li><strong>Base</strong> data of a <strong>Female </strong>subject <strong>C01</strong> recorded using a <strong>Shimmer</strong> sensor will have the name: <strong>C01_SF_base.csv</strong></li> <li><strong>After-Lunch</strong> data of a <strong>Male </strong>subject <strong>C10</strong> recorded using a <strong>Shimmer </strong>sensor will have the name: <strong>C10_SM_after_lunch.csv</strong></li> </ol> <p><strong>N.B. :</strong> As part of the <strong>MONITOR </strong>project, another <a href="https://doi.org/10.5281/zenodo.11518751" target="_blank" rel="noopener">dataset</a> titled, "<strong>MONITOR PhysiCube Dataset: Dataset of Physiological Parameters for Monitoring Stress in Garments Workers using PhysiCube Sensors</strong>", was simultaneously developed using an in-house sensor device, named <strong>PhysiCube</strong>, to measure the physiological data of the same subjects.</p>
Garment depth images - Personal Robotics Lab
<p>Real garment depth images</p>
Garment depth images_3 - Personal Robotics Lab
<p>Garment depth images_2 - Personal Robotics Lab</p>
Garment depth images_2 - Personal Robotics Lab
<p>Garment depth images_2 - Personal Robotics Lab</p>
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