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120 results for “clothing”
Dataset of "Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing"
<p>This dataset was used in the publication:<br> <strong>Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing</strong><br> presented at the IEEE International Conference on Robotics and Automation 2022</p> <p><strong>Abstract:</strong><br> Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured.<br> In this work we propose the <em>Difference Mapping</em> distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors.<br> The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2 percent for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with 10 participants. <br> Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach.<br> The experimental data for this study is available online</p> <p> </p> <p><strong>Data structure:</strong><br> The data contains trials of 12 subjects for different motions, wearing at the same time a tight setup with inertial sensors and a loose working suit with integrated inertial sensors. It contains the raw IMU data, raw Magnetometer data and the estimated segment orientations using a Sensor Fusion engine provided by Sci-Track.<br> Please note, that in the publication only the first 10 subjects were used and the upper body information was used only. The Sternum sensor of the tight setup of subjects 11, 12 and 13 tilted slowly during the long-term measurements. For this reason only 10 subjects were included in the study. However all remaining sensor of the tight setup were not tilted during recording. In particular the lower body recordings of all subjects are not corrupted.<br> <br> Code samples, a visualizer and further useful information is provided under the following git repository:<br> https://github.com/lorenzcsunikl/Dataset-of-Artefact-Aware-Human-Motion-Capture-using-Inertial-Sensors-Integrated-into-Loose-Clothing</p>
Clothing Dataset for Second-Hand Fashion
<h1>Second-Hand Fashion Dataset</h1> <h2>Update Sep. 19th, 2024</h2> <p>- Some problematic and duplicate images have been removed from version 2.<br>- All "gold dataset" data from `station1` and `station3` has been moved to a single `test100` folder.<br>- JSON errors have been fixed - all JSON files should be parsed correctly now.</p> <p>The new dataset has <strong>31,638</strong> items (+ about 100 items in `test100` folder) instead of the 31,997 items in version 2.</p> <h2>Overview</h2> <p>The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste. </p> <p>The dataset has <strong>31,638</strong> clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).</p> <h2>Project page </h2> <p>- Webpage: <a title="Project: Second-hand Fashion" href="https://fnauman.github.io/second-hand-fashion/">second-hand-fashion</a><br>- Contact: farrukh.nauman@ri.se</p> <h2>Dataset Details</h2> <p>- The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: `station1`, `station2`, and `station3`. The `station1` and `station2` folders contain images and annotations from Wargön Innovation AB, while the `station3` folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations. </p> <p>- Three images are provided for each clothing item:<br> 1. Front view.<br> 2. Back view.<br> 3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.</p> <p>- Image resolutions are primarily in two sizes: `1280x720` and `1920x1080`. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring `10x10` cm. </p> <p>- Each JSON file contains a list of annotations, some of which require nuanced interpretation (see `labels.py` for the options):<br> - `usage`: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories.<br> - `trend`: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.'<br> - `material`: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label.<br> - Damage-related attributes include:<br> - `condition` (1-5 scale, 5 being the best)<br> - `pilling` (1-5 scale, 5 meaning no pilling)<br> - `stains`, `holes`, `smell` (each with options 'None,' 'Minor,' 'Major'). <br> <br> Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For `station1` and `station2`, we introduced additional damage location labels to assist in damage detection: </p> <pre><code> "damageimage": "back", "damageloc": "bottom left", "damage": "stain ", "damage2image": "front", "damage2loc": "None", "damage2": "", "damage3image": "back", "damage3loc": "bottom right", "damage3": "stain"</code></pre> <p> Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.<br> - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. </p> <p>- Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., `pilling`).</p> <p>- Gold dataset: 100 garments were annotated multiple times by different annotators for **annotator agreement comparisons**. These 100 garments are placed inside a separate folder `test100`.</p> <p>- The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB. </p> <p>- Some attributes, such as `price`, should be considered with caution. Many distinct pricing models exist in the second-hand industry:<br> - Price by weight<br> - Price by brand and demand (similar to first-hand fashion)<br> - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK)<br> <br> Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (`station1` and `station2`). Myrorna AB (`station3`), in contrast, does resale and sets the prices. </p> <h3>Comments</h3> <p>- We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1. <br>- Some users did not prefer a `tar.gz` format that we uploaded in version 1 of the dataset. We have now switched to `.zip` for convenience.<br>- Extra care was taken not to leak personal information. This is why you will not see any entries for `annotator` attribute in the JSON files in <em>station1/sep2023</em> since people used their real names. Since then, we used internally assigned IDs. <br>- Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. <br>- Please <strong>inform us immediately</strong> if you find any personal information revelations in the dataset:<br> - Farrukh Nauman (RISE AB): `farrukh.nauman@ri.se`, <br> - Susanne Eriksson (Wargön Innovation AB): `susanne.eriksson@wargoninnovation.se`, <br> - Gabriella Engstrom (Wargön Innovation AB): `gabriella.engstrom@wargoninnovation.se`.</p> <p>We went through 100k images <strong>four</strong> times to ensure no personal information is leaked, but we are human and can make mistakes.</p> <h2>Partners</h2> <p>The data collection for this dataset has been carried out in collaboration with the following partners:</p> <p>1. <a href="https://www.ri.se/en"><strong>RISE Research Institutes of Sweden AB</strong></a>: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.</p> <p>2. <a href="https://wargoninnovation.se/en/"><strong>Wargön Innovation AB</strong></a>: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.</p> <p>3. <a href="https://www.myrorna.se/"><strong>Myrorna AB</strong></a>: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused. </p> <h2>License</h2> <p>CC-BY 4.0. Please refer to the LICENSE file for more details. </p> <h2>Acknowledgments</h2> <p>This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.</p>
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Dataset - Learning to Measure Static Friction Coefficient in Cloth Contact
<p>Dataset for: Learning to Measure the Static Friction Coefficient in Cloth Contact</p>
aRTF Clothes dataset
<p>A dataset of almost 2000 RGB images of clothes in real-world household scenes. All images* are annotated with semantic keypoints, object masks and object bounding boxes using the <a href="https://cocodataset.org/#format-data">COCO keypoints </a>format. </p> <p> </p> <p>The table below summarizes the dataset. Note that the scenes and cloth items for the train and test split are completely distinct to measure generalization across scenes and cloth items.</p> <table> <tbody> <tr> <td> </td> <td># Scenes</td> <td> </td> <td># Clothes</td> <td> </td> <td># Images</td> <td> </td> </tr> <tr> <td> </td> <td>Train</td> <td>Test</td> <td>Train</td> <td>Test</td> <td>Train</td> <td>Test</td> </tr> <tr> <td> <p>Tshirts</p> </td> <td>6</td> <td>8</td> <td>15</td> <td>20</td> <td>210</td> <td>400</td> </tr> <tr> <td>Towels</td> <td>6</td> <td>8</td> <td>15</td> <td>20</td> <td>210</td> <td>400</td> </tr> <tr> <td>Shorts</td> <td>6</td> <td>8</td> <td>8</td> <td>9</td> <td>112</td> <td>180</td> </tr> <tr> <td>Boxershorts</td> <td>6</td> <td>8</td> <td>11</td> <td>11</td> <td>154</td> <td>220</td> </tr> <tr> <td>Total</td> <td>6</td> <td>8</td> <td>49</td> <td>60</td> <td>686</td> <td>1200</td> </tr> </tbody> </table> <p> </p> <p>* Not all boxershort images have been labeled yet.</p> <h3>Codebase </h3> <p>The codebased used to capture the images and label the dataset is available <a href="https://github.com/tlpss/aRTF-Clothes-dataset" target="_blank" rel="noopener">here</a>. </p> <h3>Files</h3> <p>There are three .zip files available:</p> <ul> <li>`aRTFClothes-rgb.zip` contains all full-sized images, with COCO and CVAT annotations.</li> <li> `aRTFClothes-resized-paper-splits` contains 3 zips that each contain the train/val/test splits (as coco datasets) that were used in the paper. All images are resized to 512x256.</li> <li>`artf-all-categories_resized_512x256.zip` contains train/val/test splits that combine the cloth categories</li> </ul> <p> </p> <p>The dataset was released in <a href="https://arxiv.org/abs/2401.01734">this</a> paper, if you use the dataset in scientific publications we ask to cite this work.</p>
Resuspension of Inhalable Particles from Clothing: A Manikin-Based Chamber Study
<p>These data are part of the study 'Resuspension of Inhalable Particles from Human Clothing: A Manikin-Based Chamber Study.'</p> <p>The videos of a thermal manikin performing upper body movements at 0.25 Hz, 0.5 Hz, and 0.75 Hz, corresponding to experimental IDs 1, 2, and 3.</p>
Dataset for Benchmarking the Sim-to-Real Gap in Cloth Manipulation
<p>This dataset is supplemental to the paper "Benchmarking the Sim-to-Real Gap in Cloth Manipulation".</p> <p>D. Blanco-Mulero, O. Barbany, G. Alcan, A. Colomé, C. Torras and V. Kyrki, "Benchmarking the Sim-to-Real Gap in Cloth Manipulation," in IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2981-2988, March 2024, doi: 10.1109/LRA.2024.3360814</p>
Fig. 2 in Wolf in a sheep's clothes: juvenile coney (Cephalopholis fulva) as an aggressive mimic of the brown chromis (Chromis multilineata)
Fig. 2. Juvenile coney (above) and brown chromis (below) in dorsal view showing pale yellow spots on dorsum.
Fig. 1. A in Wolf in a sheep's clothes: juvenile coney (Cephalopholis fulva) as an aggressive mimic of the brown chromis (Chromis multilineata)
Fig. 1. A juvenile coney (Cephalopholis fulva) (circle) joins a school of brown chromis (Chromis multilineata) that is nearing a rocky reef at Fernando de Noronha Archipelago, off Northeast Brazil (above). A juvenile coney (left) and a similarly sized brown chromis (right) photographed alone.
Sustainability challenges and collaboration in the global clothing industry
<p>This dataset contains a social network of collaborative relationships among organizations working to address sustainability challenges in the global clothing industry. The dataset consists of a square matrix containing 1,455 undirected ties among 455 organizations, and a list of organizational attributes. Attribute information includes: 1) the type of organization – keystone actor, clothing company, retailer, manufacturer, trade association, multi-stakeholder platform, NGO, scientific research organization, government institution, and other; and 2) the sustainability challenges addressed by each organization – hazardous chemicals, waste reduction, energy consumption, air emissions, land use, recycling, wastewater, water conservation, and socioeconomic wellbeing. The data were gathered from corporate sustainability reports and related information found on the organizations’ websites.</p>
A Piece of Cloth on a Glass
Rainbow Code Corp, giving you a full blender work of a piece of Cloth on a Glass which can include to your work in any of animation software, let me know if there is any thing else want. Source: Objaverse 1.0 / Sketchfab
Statuette of a Woman in Hunting Clothes
ID no.: ZKWawel 5110 Museum: Wawel Royal Castle – State Art Collection http://muzea.malopolska.pl/en/obiekty/-/a/4869610/4870260 Digitalisation: RDW MIC, Małopolska's Virtual Museums Plus project Source: Objaverse 1.0 / Sketchfab
Activated Carbon Cloth Electrodes for Capacitive Deionization: A Neutron Imaging Study
<p>Neutron images of capacitive deionization by activated carbon cloths.</p><p>Corresponding readouts from potentiostat.</p><p>Nitrogen gas adsorption data of the activated carbon cloths.</p>
Panel (or Cloth) | NCMA Explore
This woven tapestry panel is embellished with the images of three warriors holding trophy heads. Each large figure is framed by rows of smaller human figures that may represent war captives. The three large warrior figures wear a Chimú-style, crescent-shaped headdress, which indicates their supernatural status... [https://learn.ncartmuseum.org/artwork/panel-perhaps-portion-of-a-loincloth-tie-end/ ](http://) *v1.4* Source: Objaverse 1.0 / Sketchfab
The floating cloth
Perhaps the cloth was once owned by royalty, of a castle of some kind. Though now it's forced to float by a old wizard and carry his magic items. While the wizard also used magic to make a sheet of cloth seem like a ghost aswell, to frighten those who come too close to the wizard's possesions. I tried to keep the texturing somewhat painterly, or atleast stylised and for some of the textures I used a ramp shader to guide me as to where the shading should be, although it is still "shadeless" I used cloth simulation to get the creases of the table cloth and ghost. The table cloth was easy enough to uv map and was one of the easiest to texture as it worked as one whole square as the UVs. Source: Objaverse 1.0 / Sketchfab
Subject-specific dimensions for designing clothing patterns
<p>The data set provided as a MATLAB (R2020a) binary file consists of 4 matrices. Each matrix has the size N times 23 and corresponds to the group of the Chinese adults (matrix adults_CN), the Czech adults (matrix adults_CZ), the Czech children (matrix children_CN) and the Czech adolescents (matrix teens_CZ). N is the number of measured subjects, where on each subjects, 23 anthropometric dimensions are measured. The dimensions in millimeters are consequently the total body height, the chest circumference (widest), the waist circumference (narrowest), the waist circumference (trousers), the hip circumference (widest), the shoulder width, the back width, the front length from neck to waist, the back length from neck to waist, the length from neck to shoulder, the sleeve length from neck to wrist, the arm circumference, the forearm circumference, the wrist circumference, the neck circumference, the length from crotch to knee, the length from crotch to ankle, the thigh circumference, the knee circumference, the calf circumference (widest), the ankle circumference, the length from waist to ground and the head circumference.</p>
Influence of product selection criteria on clothing purchase and post-purchase behaviours
<p>To analyse the influence of the attributes for sustainable clothing selection on sustainable purchase and post-purchase behaviours.</p>
Raw images corresponding to article "Relative assessment of cloth mask protection against ballistic droplets: a frugal approach"
<p>The images correspond to scans used to obtain the data reported on V. Márquez-Alvarez, J. Amigó-Vega, A. Rivera, A. J. Batista-Leyva, and E. Altshuler, <em>Relative assessment of cloth mask protection against ballistic droplets: A frugal approach</em>. PLOS ONE 17, e0275376 (2022). doi: <a href="https://doi.org/10.1371/journal.pone.0275376">10.1371/journal.pone.0275376</a>.</p>
SmartLife smart clothing gamification to promote energy-related behaviours among adolescents
<p>Inactivity and high sedentary behavior among adolescents are main societal problems. Unhealthy lifestyles place a large burden on society and promoting healthy lifestyles is thus key for health, wellness and economic prosperity. These non-communicable diseases and unhealthy lifestyles furthermore occur more often among lower socio-economic groups, which indicates a need for healthy lifestyle promotion programs to help reduce health inequalities and improve social inclusion. The SmartLife project aims to create a mobile game that requires lower body movement, and is personalized by physiological feedback measured by smart textiles. Personalization via smart textiles can present a game challenge achievable for the current fitness level of the player and can adjust this based on activity levels during game play. This approach can improve current exergames to achieve a higher level of intensity in physical activity, needed to create a health impact, and can do so considering what is achievable for the person and hence reduce drop-out and injury risks.</p>
Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p> <p>Optimization and scalability assessment of supercapacitor electrodes based on hydrothermally grown MoS2 on carbon cloth </p> <p>by Jasna Mannayil, Olli Pitkänen, Minna Mannerkorpi, and Krisztian Kordas</p>
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