Search

The measurement and analysis of human movement for applications in clinical diagnostics or rehabilitation is often performed in a laboratory setting using static motion capture devices. A growing interest in analyzing movement in everyday environments (such as the home) has prompted the development of "wearable sensors", with the most current wearable sensors being those embedded into clothing. A major issue however with the use of these fabric-embedded sensors is the undesired effect of fabric motion artefacts corrupting movement signals. In this paper, a nonparametric method is presented for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and using orthogonal regression techniques to form predictive models of the wearer's motion that eliminate these errors in the learning process. Experiments in this paper show that standard nonparametric learning techniques underperform in this fabric motion context and that improved prediction accuracy can be made by using orthogonal regression techniques. Modelling this motion artefact problem as a stochastic learning problem shows an average 77% decrease in prediction error in a body pose task using fabric-embedded sensors, compared to a kinematic model.

Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset. [ABSTRACT FROM AUTHOR]

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes. Our network learns to mimic a physics-based simulation (PBS) method while requiring two orders of magnitude less computation time. To train the network, we introduce loss terms inspired by PBS to produce plausible results and make the model collision-aware. To increase the details of the draped garment, we introduce two loss functions that penalize the difference between the curvature of the predicted cloth and PBS. Particularly, we study the impact of mean curvature normal and a novel detail-preserving loss both qualitatively and quantitatively. Our new curvature loss computes the local covariance matrices of the 3D points, and compares the Rayleigh quotients of the prediction and PBS. This leads to more details while performing favorably or comparably against the loss that considers mean curvature normal vectors in the 3D triangulated meshes. We validate our framework on four garment types for various body shapes and poses. Finally, we achieve superior performance against a recently proposed data-driven method. [ABSTRACT FROM AUTHOR]

Wireless technologies are essential components of wearable devices for applications ranging from connected healthcare to human–machine interfaces. Their performance, however, is hindered by the human body, which obstructs the propagation of wireless signals over a wide range of directions. Here, we demonstrate conformal propagation and near-omnidirectional radiation of wireless signals near the body using clothing integrated with spoof surface plasmonic (SSP) structures. These structures, fabricated entirely from conductive textiles, induce wireless signals emitted by nearby antennas to propagate around the body as surface waves and radiate as propagating waves in directions otherwise obstructed by the body. We describe the procedure for designing textile-based spoof surface plasmon waveguides, radiating elements, and impedance matching sections for operation in the 2.4–2.5 GHz industrial, scientific, and medical (ISM) band. Using a tissue phantom model of the human torso, we experimentally demonstrate 2.62 greater angular coverage by a dipole placed near the body compared to without the clothing. [ABSTRACT FROM AUTHOR]

The appearance of fabric motion is suggested to affect the human perception of bending stiffness. This study presents a novel spatial augmented reality, or projection mapping, approach that can visually manipulate the perceived bending stiffness of a fabric. Particularly, we proposed a flow enhancement method that can change the apparent fabric motion by using a simple optical flow analysis technique rather than complex physical simulations for interactive applications. Through a psychophysical experiment, we investigated the relationship between the magnification factor of our flow enhancement and the perceived bending stiffness of a fabric. Furthermore, we constructed a prototype application system that allows users to control the stiffness of a fabric without changing the actual physical fabric. By evaluating the prototype, we confirmed that the proposed technique can manipulate the perceived stiffness of various materials (i.e., cotton, polyester, and mixed cotton and linen) at an average accuracy of 90.3 percent. [ABSTRACT FROM AUTHOR]

A prototype of constant temperature drying rate (DRCT) tester (CTDRT) was built to evaluate the DRCT of fabrics. The sample platform is kept at constant temperature and was placed on the balance. The platform temperature is adjustable to simulate different skin temperatures. A heater was placed underneath the sample platform, but it did not have any contact with the sample platform. This novel noncontact heating system enables real-time measurement of water evaporation in the sample throughout the whole experiment. In addition to DRCT, comprehensive information such as water absorption, water spreading, and other fabric drying features can be obtained from CTDRT. In addition, negative pressure gradient system of CTDRT can help to maintain constant atmospheric conditions at negligible wind speed, thereby enhancing the stability and sensitivity of measurement. The DRCT, a key parameter of CTDRT, of 28 fabrics investigated is between 0.36 and 2.56 mL/h at the skin temperature of 37 °C. The validity and repeatability of CTDRT were also confirmed with statistical evidence. The testing duration for CTDRT ranges from 15 to 40 min, which is similar to those of other drying methods where heat is applied, but is much faster than the conventional nonheating methods. [ABSTRACT FROM AUTHOR]

In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented, and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions. [ABSTRACT FROM AUTHOR]

Cloth is made of yarns that are stitched together forming semi-regular patterns. Due to the complexity of stitches and patterns, the macroscopic behavior of cloth is dictated by the contact interactions between yarns, not by the mechanical properties of yarns alone. The computation of cloth mechanics at the yarn level appears as a computationally complex and costly process at first sight, due to the need to resolve many fine-scale contact interactions. We propose instead an efficient representation of cloth at the yarn level that treats yarn-yarn contacts as persistent, but with the possibility to slide, thereby avoiding expensive contact handling altogether. We introduce a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn crossings, loops, stitches, and stacks, with a minimum cost. Based on this representation, we design force models that reproduce the characteristic macroscopic behavior of yarn-based fabrics. Our approach is suited for both woven and knitted fabrics. We demonstrate the efficiency of our method on simulations with millions of degrees of freedom (hundreds of thousands of yarn loops), almost one order of magnitude faster than previous techniques. We also compare the different macroscopic behavior under woven and knitted patterns with the same yarn density. [ABSTRACT FROM PUBLISHER]

The field of wearable computing has great implications in the measurement of the human body and its surrounding environment, with potentially high impact in medical applications such as early detection, treatment, compliance monitoring, care for the frail and elderly, telemedicine, physical therapy, etc. [1]. Unfortunately, the field of wearable computing is still held back by the lack of a standardized framework and development process, forcing developers into custom and time intensive solutions. In this paper, the current progress of development of a universal smart textile system is discussed which would solve this problem and accelerate the field of smart wearable electronics, providing a universal platform for attaching several hundreds of sensors to clothing in convenient, unobtrusive and energy efficient ways. The main problems and potential solutions discussed in this article include power and data transfer wire topology, allowing the textile to be arbitrarily cut and sewn into smart clothing, data transmission architecture, providing high bandwidth and low energy data transfer, and support for miniaturization and elasticity, making the potential end product as unobtrusive as possible. [ABSTRACT FROM PUBLISHER]

Developments in computer capabilities, networks, mobile devices, and social media have enabled researchers and educators to increasingly employ blended strategies when developing new learning technologies. In addition, there is a growing need to develop discipline-specific learning technologies as result of expanding research in various domains. The study reported here was conducted in the context of a textile and clothing higher education course. The key aim was to develop a new learning technology for university students, specifically to help them learn about clothing thermal functional design (CTFD). [ABSTRACT FROM PUBLISHER]

An activity classifier based on an abstract model of the human body is suitable for use in an e-textile monitoring system without the need to retrain the recognition model to accommodate different users, sensor types, or garments. [ABSTRACT FROM AUTHOR]

Current technology supports only special-purpose, low-volume textiles, garments, and electronics. Moreover, the textile, electronic, and software industries have different product cycles, cultures, and price models, creating scores of practical problems for smart textiles. Mass producing smart cloth will require decoupling the textile production from concrete sensing apps and moving the complexity to generic electronics and software--creating wearable sensing as an app. [ABSTRACT FROM AUTHOR]

The International Electrotechnical Commission Standard 61482-1 describes procedures for measuring the thermal performance of fabrics and garments intended for use in clothing for workers exposed to electric arcs. This paper demonstrates that the tests in this Standard fail to produce controlled laboratory conditions. The dominant magnetic and thermal buoyancy forces have been minimized leaving two undefined and uncontrolled forces to randomly dominate arc motion, plasma flow, and thermal stress. The tests also produce hazards significantly different from those faced by victims in real situations. The velocity of the plasma impinging on a victim is incorrect. The color of the radiation is incorrect. The molten metal droplets impinging on the victim are incorrect. The tests only consider clothing stretched over a surface, whereas loose and unbacked cloth suffers considerably more stress. In order to emulate real arcing faults, it will be necessary to use parallel copper and aluminum electrodes. In addition, for personnel working on high-voltage, a “pop-out” test needs to be developed because a fault current that starts by entering and exiting the body becomes an arc inside the clothing. [ABSTRACT FROM PUBLISHER]