Search Results (89)

The Latin American Development Group for Maturity (GDLAM) protocol has been widely used to assess functional autonomy (FA) in community-dwelling older adults. However, to date, no percentile-based rating scale has been established for older Chilean women living in the community. The aim was to create a percentile-based rating scale for the GDLAM protocol in community-dwelling older Chilean women. 347 older women volunteered for this study. The sample was divided into five groups by age ranges (G1: 60.0–64.9 years, G2: 65.0–69.9 years, G3: 70.0–74.9 years, G4: 75.0–79.9 years, and G5: ≥80.0 years). The research had an observational, cross-sectional design with a descriptive strategy. The GDLAM protocol included (a) Putting on and taking off a T-shirt (PTS), (b) Standing up from the sitting position (SSP), (c) Standing up from the prone position (SPP), (d) Walking 10 m (W10 m), and (e) Sit-ting and getting up from the chair and moving around the house (SCMA), all assessed in seconds (s), while the General Index of Autonomy (GI) was calculated in points (p). The percentile-based classification was developed with the following thresholds: percentile ≤ 0.15: “Very Good,” percentile 0.16–0.49: “Good,” percentile 0.50–084: “Regular,” and percentile ≥ 0.85: “Poor.” After creating the percentile-based ranking scale for the five age ranges, it was observed that the older the age, the lower the FA, as represented by the five tests and the GI. The percentile-based ranking scale presented in this research will enable us to accurately assess and interpret the FA of older and community-dwelling women in Chile. However, the study is limited to women and cannot be generalized to older adults in general. Full article

This study presents the first cradle-to-gate life cycle assessment (LCA) of T-shirt production using viscose and Lyocell fibers, benchmarked against cotton and polyester under consistent system boundaries. The analysis covers spinning, knitting, wet processing, garment assembly, and regionalized energy supply. Results show that cotton T-shirts exhibit the lowest global warming potential (14.1 kg CO₂eq/kg) but the highest water demand (2.9 m³/kg) in China. Polyester garments, although less water-intensive, contribute significantly to plastic accumulation (1.0 kg/kg shirt) compared to cellulose-based fibers (0.1 kg/kg shirt). Within man-made cellulose fibers, Lyocell generally outperforms viscose in toxicity-related categories—reducing freshwater ecotoxicity by 35% and human non-carcinogenic toxicity by 62%—thanks to its closed-loop solvent recovery. However, Lyocell also shows the highest carbon footprint (21.6 kg CO₂eq/kg) unless produced in regions with cleaner energy mixes. Regional sensitivity analysis indicates that shifting production from China to Brazil could reduce global warming impacts by up to 38%. Overall, these results highlight the trade-offs across fiber types and demonstrate the importance of both material choice and production geography in driving sustainability within textile supply chains. Full article

Clothing pressure influences the comfort, mobility, and welfare of dogs; however, quantitative evidence on how obesity affects localized garment pressure is limited. Using CLO 3D virtual fitting, we evaluated clothing pressure according to body condition (normal vs. obese), posture, and fabric type. We constructed normal and obese avatars for three breeds and simulated a short-sleeved T-shirt across six postures and three fabrics, yielding n = 108 simulation conditions (two body conditions × three breeds × six postures × three fabrics). Clothing pressure was quantified as ROI-averaged pressure (kPa) at four body regions (P1–P4). The overall mean pressure (averaged across P1–P4) increased from 16.69 ± 3.69 kPa (normal) to 19.56 ± 5.03 kPa (obese), with the highest pressures consistently observed at the chest (P2) and abdomen (P4). Region-specific ANOVA/GLM analyses (breed treated as a fixed factor) showed significant main effects of body condition, posture, fabric type, and breed on clothing pressure (all p < 0.001), while the three-way interaction (body condition × posture × fabric) was not significant (p > 0.05). These findings show that CLO 3D virtual fitting enables controlled, simulation-based comparisons of clothing pressure across body conditions; however, because no in vivo wear trials were conducted, the results should be interpreted as preliminary, and they require future experimental validation before practical application. Full article

The sustainability transition of the textile industry requires amongst other strategies circular approaches. Ecodesign guidelines and design for recycling are approaches that reduce resource consumption and textile waste. Garments are made of a large variety of different materials, from blended fibers to haberdashery items, colorants, and finishings, making it challenging to predict the composition of post-consumer textile waste. This mix of materials complicates recycling efforts, contributing to globally less than 1% of fiber-to-fiber recycling. This study investigates material compositions of one of the most popular and widespread garments: T-shirts. While about half of our sample contains cotton only, the other items contain two or more fibers, revealing huge variations in fiber blends, including varying degrees of elastane contents, which are not linked to functional requirements. These blends, especially the varying levels of elastane, increase costs and efforts for recycling, making fiber-to-fiber recycling less attractive and more expensive than new fiber production. They also contribute to avoidable microfiber pollution. Accordingly, this study underlines the requirements for providing detailed ecodesign guidelines and applying the extended producer responsibility to incorporate environmental lifecycle costs, to help shift the industry towards a circular economy. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Augmented reality (AR) technologies—such as Instagram filters—bridge the digital and physical worlds by allowing users to virtually try on clothing, thereby reducing the risk of virus transmission. In the T-shirt retail industry, AR enables product personalization, decreases the need for physical production, minimizes textile waste, and lowers carbon emissions. It also benefits individuals with limited mobility or those who prefer shopping online. This study tested several hypotheses on 105 active Instagram filter users using filters from the ’Apprecio’ account on mobile devices. Data analyzed using the partial least squares method revealed that interactivity significantly influences both purchase intention and continued use of digital platforms. While hedonic and vivid features enhance the user experience, they have a limited impact on driving purchases or long-term engagement. Customers’ engagement and buying intent are more strongly shaped by practical and interactive elements. The study recommends that companies invest in developing interactive AR features to boost customer satisfaction and foster trust. Future research should involve larger participant samples and investigate specific interactive elements—such as virtual try-on tools—to better understand their impact on consumer behavior. This study highlights the critical role of interactivity in AR for delivering meaningful and engaging shopping experiences. Full article

Consumers are increasingly willing to choose more sustainable products, driven by affordability and sustainability considerations. However, they often face difficulties in understanding the multitude of product certifications and identifying “greenwashing” marketing claims. This highlights the need for a clear and harmonized sustainability scoring system that allows consumers to benchmark products. Sustainability encompasses three key pillars: environmental, social, and economic. Accurately scoring a product’s sustainability requires addressing a wide range of criteria within these pillars, introducing significant complexity. This study proposes a multicriteria methodology for scoring the sustainability of apparel products into an A to E label. The approach combines a life cycle assessment covering environmental impacts from “farm-to-gate”, with a social evaluation based on country-level social key performance indicators (KPIs) and factory-specific data aligned with the International Labour Organization (ILO). Additionally, the sustainability score incorporates the impact of product durability, as longer-lasting products can reduce environmental footprint and costs for consumers. The methodology is defined and validated through a case study of a white T-shirt produced with 50% recycled cotton and 50% organic cotton. The results demonstrate the comprehensive assessment of the T-shirt’s environmental and social impacts, providing a detailed sustainability score, highlighting the role of recyclability. This comprehensive sustainability scoring system aims to provide consumers with a clear, harmonized, and reliable assessment of product sustainability, empowering everyone to make informed purchasing decisions aligned with their values. It will also enable brands and retailers to calculate the sustainability score of their products, including in the scope of digital product passport, provided they can ensure traceability and transparency along the supply chain. Full article

This paper introduces a novel Underclothing Body Shape Perception Network (UBSP-Net) for reconstructing parametric 3D human models from clothed full-body 3D scans, addressing the challenge of estimating body shape and pose beneath clothing. Our approach simultaneously predicts both the internal body point cloud and a reference point cloud for the SMPL model, with point-to-point correspondence, leveraging the external scan as an initial approximation to enhance the model’s stability and computational efficiency. By learning point offsets and incorporating body part label probabilities, the network achieves accurate internal body shape inference, enabling reliable Skinned Multi-Person Linear (SMPL) human body model registration. Furthermore, we optimize the SMPL+D human model parameters to reconstruct the clothed human model, accommodating common clothing types, such as T-shirts, shirts, and pants. Evaluated on the CAPE dataset, our method outperforms mainstream approaches, achieving significantly lower Chamfer distance errors and faster inference times. The proposed automated pipeline ensures accurate and efficient reconstruction, even with sparse or incomplete scans, and demonstrates robustness on real-world Thuman2.0 dataset scans. This work advances parametric human modeling by providing a scalable and privacy-preserving solution for applications to 3D shape analysis, virtual try-ons, and animation. Full article

This study investigates the effects of waste for value addition in form of use of textile waste to comfortable and durable garments based on blending recycled cotton fibers extracted from spinning, weaving, and cutting waste with virgin cotton in different ratios of 70:30, 80:20, and 90:10 to produce yarns of 22/1 count, which are used to develop single jersey knitted T-Shirt, examining key properties such as mechanical and thermos-physiological properties. Grey fabric (unprocessed fabric) with a higher virgin cotton content and from spinning waste exhibited superior bursting strength, overall moisture management capacity, and thermal conductivity. In contrast, air permeability and water vapor permeability were highest in fabric made with weaving waste. After scouring and bleaching, the finished fabric (processed fabric) was compared with the grey fabrics. The results demonstrate that the finished fabric has slightly reduced bursting strength, water vapor permeability, and moisture management capacity while significantly enhancing air permeability and maintaining thermal conductivity. T-shirt properties were evaluated across various blend ratios and waste types over multiple washing cycles. Overall, the study demonstrates that recycled cotton fibers, particularly those from spinning waste, can be successfully produced into high-performance knitted t-shirts, offering a sustainable alternative to fully virgin cotton products without compromising performance significantly. Full article

Opto-electronic plethysmography (OEP) is used to measure chest wall compartment volumes and their synchronisation. Breathing pattern disorder (BPD) can be distinguished using the phase angles between these chest wall compartments during exercise. However, the time taken to manually place the standard OEP model involving 89 reflective markers is high during clinical application. The purpose of this study was to investigate the use of a pre-markered T-shirt instead of markers applied directly to the skin at rest, during different exercise intensities and recovery. Thirty-nine healthy participants (24 male, 15 female) aged 18–40 years performed an incremental cycling test with the skin-mounted OEP marker set. Participants then repeated the same cycling test with a pre-markered T-shirt. Across all test conditions, the T-shirt showed a strong level of agreement (Intraclass correlation coefficient (ICC) ≥ 0.9) with the standard breath-by-breath (BbB) gas analyser. Moreover, ICC values exceeded 0.8 for compartment contributions across all test conditions, indicating excellent agreement with the skin-mounted markers. The phase angles between compartments showed the best agreement during the moderate exercise level (0.6 < ICC < 0.8). In conclusion, the pre-markered T-shirt presents a viable solution for the quick monitoring of breathing patterns, as well as an effective tool in diagnosing BPD during exercise. Full article

This paper presents a study on the repeatability of washing effects on two antenna-based sensors for breathing monitoring. One sensor is an embroidered meander antenna-based sensor integrated into a T-shirt, and the other is a loop antenna integrated into a belt. Both sensors were subjected to five washing cycles, and their performance was assessed after each wash. The embroidered meander antenna was specifically compared before and after washing to monitor a male volunteer’s different breathing patterns, that is, eupnea, apnea, hypopnea, and hyperpnea. Stretching tests were also conducted to evaluate the impact of mechanical deformation on sensor behavior. The results highlight the changes in sensor performance across multiple washes and stretching conditions, offering insights into the durability and reliability of these embroidered and loop antennas for practical applications in wearable health monitoring. The findings emphasize the importance of considering both washing and mechanical stress in the design of robust antenna-based sensors. Full article

Online shopping has become popular due to its convenience and potential cost savings. However, clothing size cannot be accurately estimated, particularly when buying shirts. Many shoppers provide size choices but with inaccurate fits. To assist users in selecting the correct size when purchasing t-shirts online, we estimated shirt size using calculated upper body dimensions. Computer vision algorithms, including YOLO, PoseNet, body contour detection, and a trained convolutional neural network (CNN) model were employed to estimate shirt sizes from 2D images. The model was tested using images of 30 participants taken at a distance of 180–185 cm away from a Raspberry Pi camera. The estimation accuracy was 70%. Inaccurate predictions were attributed to the precision of body measurements from computer vision and image quality, which needs to be solved in further studies. Full article

Thermal and moisture properties of the textile materials worn in close contact with the skin greatly contribute to the comfort of the workwear and of the personal protective clothing (PPC) assemblies they are part of. This study examines in depth the thermoregulatory properties of eighteen knitted fabrics used in polo shirts and T-shirts, which function as thermal underwear, standard workwear compliant with various regulations, or as base layers in PPC systems. Most of the fabrics specifically engineered for heat protection demonstrated superior air permeability (ranging from 700 to 1200 mm/s) and efficient moisture management (OMMC 0.5–0.7). Their drying time varied between 12 and 18 min, comparable to most commodity fibre blend fabrics investigated. Generally, the heat-protective fabrics were heavier and exhibited greater thermal and vapour resistance. However, despite minor variations in predicted thermal comfort, seventeen of the fabrics were classified in the same cluster. These findings offer valuable insights into the thermal and moisture management properties of knitted fabrics with various levels of protection, and the correlations found between their thermoregulatory and physical properties, such as mass and thickness, provide guidance for the development of innovative knitted materials for workwear that enhance wearer comfort. Full article

The enzymatic hydrolysis process is important in the field of textile waste reuse in the circular economy context. Currently, enzymatic cellulase treatment of waste textiles, such as bamboo mixture with spandex samples (BS), cotton jeans (CJ), linen (L), and cotton T-shirts (CT), has been tested, in which glucose production was measured at the presence of 6 and 8% NaOH solution. The characteristics of the textiles and hydrolysis capacity were evaluated by the amount of glucose (g) obtained from each textile. The following indicators were also measured during the experiment: temperature, pH, enzymatic cellulase solution composition, final glucose concentrations, turbidity, and color intensity. The temperature of the mixture was maintained at 50 °C, and a pH level of 5–7 along with a contact time of 48–94 min were controlled. The experiments demonstrated that when the enzymatic hydrolysis was active, turbidity increased from 86 nephelometric turbidity unit (NTU) to >1000 NTU; the color of the hydrolyzed samples was obtained from 86 NTU to >1000 NTU; and the final glucose concentration was approximately between 0.49 and 33.9 mmol/L for L, CT, and CJ samples measured to produce up to one gram of glucose from 3.330 g of textile, and a BS samples produced one gram of glucose from 3.164 g of textile. The findings show that recycled glucose obtained from textile waste materials is environmentally sustainable. Such textile waste can then be reused rather than being dumped in already overloaded landfills. Full article