Search Results (34)

Effective personal wardrobe management remains a significant challenge, often leading to underutilized garments and increased fashion waste. Existing digital wardrobe solutions commonly lack intelligent capabilities such as automated organization, personalized styling support, and immersive visualization. We developed StyleVision, an AI-integrated wardrobe stylist application that addresses these limitations. StyleVision incorporates a comprehensive AI pipeline, beginning with standardized garment image preprocessing. The system comprises deep learning-based hierarchical garment classification, a dedicated model for outfit esthetic evaluation utilizing synthesized 2D images, and advanced 2D and 3D visualization modules that facilitate outfit exploration and spatial assessment through static 3D garment representations. To improve prediction reliability, confidence thresholding mechanisms are applied across all predictive components. An experimental evaluation on a custom dataset demonstrated robust performance, achieving high accuracy in garment classification and yielding solid results in style classification. The 3D visualization module was functionally validated, producing realistic and distinguishable visual outputs. By offering intelligent styling and interactive visualization, StyleVision enhances wardrobe utilization and encourages more sustainable fashion consumption practices. Full article

Chronic venous insufficiency (CVI) is a common peripheral vascular condition characterised by the retrograde blood flow in the lower extremities and its consequences such as oedema and other complications. Clinical severity of CVI is assessed according to the CEAP (Clinical, Etiological, Anatomic, and Physiopathologic) classification, which recognises seven grades of increasing clinical severity (C₀–C₆). Compression therapy aims to accelerate vein, lymph, and microcirculation flow and therefore reduce chronic nonbacterial inflammation and oedema of the extremities. In accordance with the elasticity and stiffness, compression bandages and garments are divided into short-stretch and long-stretch compression materials. Compression therapy is applicable in all stages of CVI. Moreover, compression therapy in conjunction with physical therapy and lifestyle modifications is more effective in reducing oedema, preventing venous distension, and reducing venous wall tension, all while improving calf muscle pump function. Physical therapy in CVI treatment combines everyday lifestyle modifications, physical activity, medical exercise, sports activity, hydrotherapy, and electrotherapy. Therefore, physical therapy is used either for prevention or either for therapeutic purposes in CVI. For grades CEAP C₀–C_2, preventive measures consist of education and counselling, medical exercise and general fitness, and sports and physical activities. However, for therapy in grades CEAP C₃–C₆, medical exercise and a specific rehabilitation programme, manual lymphatic drainage and massage, balneotherapy, and electrotherapy are recommended. Full article

The garment manufacturing industry, being labor-intensive, has long faced challenges in automating the sewing process due to the flexibility and deformability of fabrics. This study proposes a novel strategy for automated sewing by integrating soft fingers and machine vision technology. Firstly, leveraging the flexibility and adjustability of soft fingers, combined with the motion characteristics of the sewing machine, a sewing model was established to achieve coordinated operation between the soft fingers and the sewing machine. Experimental results indicate that the fabric feeding speed and waiting time of the soft fingers are significantly correlated with the sewing speed and stitch density of the sewing machine, but not with the fabric properties. Secondly, machine vision technology was employed to inspect the quality of the sewn fabrics, achieving a classification accuracy of 97.84%. This study not only provides theoretical and technical support for the intelligent upgrading of the garment manufacturing industry but also lays the foundation for the automation of complex sewing processes such as quilting. Future research will further optimize the system’s performance and expand its applications in more complex sewing tasks. Full article

Recent pressure sensor research often focuses on developing sensors for impulse applications, including touch sensors, e-skin development, or physiological monitoring. However, static loading applications, such as those needed for compression garment design, are significantly under-researched in comparison. Many technology solutions do not translate across applications, as static loading requires measurements which have high accuracy, high precision, and low drift. To address the gap in sensor development between impulse and static applications, we define a literature-based taxonomy providing two conceptual classifications based on sensor functionality and specific design characteristics. The taxonomy’s utility is demonstrated through the mapping of sensors onto compression garment development phases by matching application requirements with sensor performance. The taxonomy developed will advance research and the industry by providing a roadmap of how sensor characteristics influence performance to drive a focused development for future sensors, specifically for compression garment innovation. Full article

The aim of this study was to compare the types, textile structures, labels, and fiber compositions of 64 red garments submitted as evidence in selected criminal cases between 2022 and 2024. The research enhanced the current knowledge of the characteristics of red clothing available to consumers and demonstrated the relevance of textile analysis in forensic science. Knitted fabrics were the most commonly used in the garments, followed by woven fabrics, nonwovens, and felts. Fiber identification focused on color and shade, generic classification, morphological structure, and chemical composition, revealing both similarities and distinctions among the samples. In a small percentage of cases, label information was found to be inaccurate. The study also examined the fiber content of threads, patches, logos, prints, and embroidery, underscoring the forensic potential of these often-overlooked elements. The identification of over 300 individual fibers enabled a critical evaluation of the analytical procedures and confirmed their effectiveness in forensic contexts. Full article

Cashmere is widely acclaimed as one of the most luxurious textile fibers. Mongolia, a major player in cashmere production and processing, is key to this industry. Despite the rich history of cashmere, there is limited research on cashmere fiber properties, which are essential in producing high-quality garments. This study aims to improve our understanding of cashmere fibers’ physical and mechanical properties and to assess how genetic and non-genetic factors affect these characteristics. We analyzed key fiber characteristics, including scale morphology, and the physical and mechanical properties (such as fineness, length parameters, stress, and strain) in 11 samples from Mongolian goats of varying areas, breeds, ages, and genders. Through detailed statistical analysis, our experimental results revealed that both genetic and non-genetic factors significantly affect fiber fineness and the specific energy of rupture. Additionally, we observed that the influence of these factors can inform better classification systems for raw cashmere and enhance the determination of the fiber’s spinability limit. Full article

Analysis of functional products for medical textiles indicates that there are plenty of different classifications of this group. Requirements for compression generated by compression garments differ depending on the application area, and even more, sometimes are contradictory and can be fulfilled in very different ways. The effectiveness of such products depends on mechanical and physical properties as well as psychological barriers. Currently, there is no uniform classification of compression classes, furthermore, there is no uniform standard, test method or technic for evaluation of the product’ compression. Knitted compression fabrics are made by knitting together at least two types of yarns: a ground yarn which ensures stiffness and thickness and an elastomeric yarn which generates compression. Knitted compression products can be produced on both flat and circular knitting machines, though parameters and usage of production are different. Additional elements used in the structure of the compression product can significantly change the generated compression. Purposes and number of additional details depend on the application and functionality of the compression support, nevertheless, all rigid elements must be taken into account at the designing stage. Additional functionality like antimicrobial activity or thermal therapy can also be provided for compression knits. It is highly important to ensure the longevity of all functional properties. Full article

The rapid expansion of e-commerce, particularly in the clothing sector, has led to a significant demand for an effective clothing industry. This study presents a novel two-stage image recognition method. Our approach distinctively combines human keypoint detection, object detection, and classification methods into a two-stage structure. Initially, we utilize open-source libraries, namely OpenPose and Dlib, for accurate human keypoint detection, followed by a custom cropping logic for extracting body part boxes. In the second stage, we employ a blend of Harris Corner, Canny Edge, and skin pixel detection integrated with VGG16 and support vector machine (SVM) models. This configuration allows the bounding boxes to identify ten unique attributes, encompassing facial features and detailed aspects of clothing. Conclusively, the experiment yielded an overall recognition accuracy of 81.4% for tops and 85.72% for bottoms, highlighting the efficacy of the applied methodologies in garment categorization. Full article

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing. Full article

The integration of sensors into garments has paved the way for human activity recognition (AR), enabling users to engage in extended human motion recordings. The inherent fluidity of loose clothing allows it to mirror the wearer’s movements. From a statistical standpoint, clothing captures additional valuable insights beyond rigid body motions, improving AR. This work demonstrates how fabric’s orientation, layering and width contribute to the enhanced performance of AR with clothing in periodic motion. Experiments are reported in which a scotch yoke and a KUKA robot manipulator are used to induce the periodic motion of fabric cloth at different frequencies. These reveal that clothing-attached sensors exhibit higher frequency classification accuracy among sensors with an improvement of 27% for perpendicular-oriented fabric, 18% for triple-layered fabric, and 9% for large-width fabric, exceeding that seen with rigid attached sensors. Full article

Haemodorum coccineum, commonly known as scarlet bloodroot, is a plant native to New Guinea and the northern most parts of Australia. The highly coloured H. coccineum is used by communities in Larrakia country for dyeing garments and occasionally to treat snake bites. Previous studies into H. coccineum have focused on its taxonomic classification, with this being the first evaluation of the chemical composition of the plant. Haemodoraceae plants are reported to contain phenylphenalenones (PhPs), which are highly conjugated polycyclic oxygenated aromatic hydrocarbons. We report the characterisation of 20 compounds extracted from the rhizome of H. coccineum: four sugars and 16 compounds belonging to the PhP family. The compounds include five aglycones and seven glycosylated compounds, of which four contain malonate esters in their structures. Characterisation of these compounds was achieved through 1D and 2D NMR, MS analysis and comparison to the known phytochemistry of other species from the Haemodorum genus. Preliminary anti-microbial activity of the crude extract shows significant inhibition of the growth of both gram-positive and gram-negative bacteria, but no activity against Candida albicans. Full article

The aim of this study is to evaluate the effectiveness of electrosuit therapy in the clinical treatment of children with Cerebral Palsy, focusing on the effect of the therapy on spasticity and trunk control. Moreover, the compliance of caregivers with respect to the use of the tool was investigated. During the period ranging from 2019 to 2022, a total of 26 children (18 M and 8 F), clinically stable and affected by CP and attending the Neurorehabilitation Unit of the “Padre Pio Foundation and Rehabilitation Centers”, were enrolled in this study. A subset of 12 patients bought or rented the device; thus, they received the administration of the EMS-based therapy for one month, whereas the others received only one-hour training to evaluate the feasibility (by the caregivers) and short-term effects. The Gross Motor Function Classification System was utilized to evaluate gross motor functions and to classify the study sample, while the MAS and the LSS were employed to assess the outcomes of the EMS-based therapy. Moreover, between 80% and 90% of the study sample were satisfied with the safety, ease of use, comfort, adjustment, and after-sales service. Following a single session of electrical stimulation with EMS, patients exhibited a statistically significant enhancement in trunk control. For those who continued this study, the subscale of the QUEST with the best score was adaptability (0.74 ± 0.85), followed by competence (0.67 ± 0.70) and self-esteem (0.59 ± 0.60). This study investigates the impact of the employment of the EMS on CP children’s ability to maintain trunk control. Specifically, after undergoing a single EMS session, LSS showed a discernible improvement in children’s trunk control. In addition, the QUEST and the PIADS questionnaires demonstrated a good acceptability and satisfaction of the garment by the patients and the caregivers. Full article

Introduction: The pathologic features of fatty tissue in lipedema are often challenging to diagnose, thus allowing for variable bias and leading to underdiagnosis. Lipedema is a disease that is currently little known worldwide, but it represents a public health problem and demands immediate, well-directed healthcare. Insufficient scientific information limits medical action, which limits making diagnoses and addressing an adequate multidisciplinary treatment. This study aims to evaluate the current state of lipedema in Spain to contextualize the disease’s pathophysiological characteristics and thus achieve a consensus that unifies and defines its diagnostic criteria and medical management. Likewise, this study aims to determine the effectiveness of the various treatments applied to the study patients and to evaluate the consequences of the pandemic related to this disease. Material and methods: The present work is a descriptive, cross-sectional study that analyzed online questionnaires. It was applied to 1069 patients and collected over 9 months between 2021 and 2022. The questionnaires were distributed to the leading national and regional associations of patients affected by lipedema. The study included all patients in a group who had a diagnosis of lipedema and in a group of undiagnosed patients with six or more symptoms. The variables analyzed were age, weight, height, body mass index (BMI), type of lipedema (according to Schingale’s classification), symptoms (according to Wolf’s classification, modified by Herbst), and treatments performed (physiotherapy, compression garments, sports, diet, radiofrequency, mesotherapy, and surgery), associated with the score given by the patients regarding the degree of improvement in their disease with each of these treatments. Results: There were 967 women and 2 men between 18 and 75 years old (mean of 38.5 years); a body weight between 33 and 150 kg (mean 75.8 kg); a height between 144 and 180 cm (mean 164 cm); and an average body mass index (BMI) of 28.1. The most common kind of lipedema in our study population was type III (affecting the hips, thighs, and calves). The treatment that individually improved patients’ quality of life the most was surgery, only surpassed by the multidisciplinary approach to the disease, including conservative measures. Conclusions: With this study, we can conclude that, in Spain, there is a real problem associated with the diagnosis of lipedema, specifying the need to seek this diagnosis actively and propose multidisciplinary management, since it offers the best overall results, of course not without forgetting that surgery is one of the most critical pillars in the approach to this disease. Consistent with the results obtained in this study, criteria were proposed and applied to represent a statistical value at the time of ruling on the clinical diagnosis of lipedema, considering that a patient who presents six or more of these diagnostic criteria, with a very high probability, will have lipedema. Full article

As an important parameter of garment comfort, the thermal sensation of fabrics changes with factors such as sweat-induced humidity, making it a crucial area of research. To explore the coolness sensation of fabrics under different humidities, we tested heat transfer between fabrics and skin for 20 different fabrics with varying thermal absorption rates using fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was then obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness levels. We compared the objective and subjective evaluations and found them to be highly consistent (R² = 0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance predictions. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. We established a random forest regression model for predicting the coolness of wet fabrics, obtaining a high accuracy between predicted and tested thermal absorption coefficients (R² = 0.872, RMSE = 0.305). Therefore, our random forest regression model can successfully predict the coolness of wet fabrics. Full article

Research on healthcare and body monitoring has increased in recent years, with respiratory data being one of the most important factors. Respiratory measurements can help prevent diseases and recognize movements. Therefore, in this study, we measured respiratory data using a capacitance-based sensor garment with conductive electrodes. To determine the most stable measurement frequency, we conducted experiments using a porous Eco-flex and selected 45 kHz as the most stable frequency. Next, we trained a 1D convolutional neural network (CNN) model, which is a type of deep learning model, to classify the respiratory data according to four movements (standing, walking, fast walking, and running) using one input. The final test accuracy for classification was >95%. Therefore, the sensor garment developed in this study can measure respiratory data for four movements and classify them using deep learning, making it a versatile wearable in the form of a textile. We expect that this method will advance in various healthcare fields. Full article