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Monitoring human movement is highly relevant in mobile health applications. Textile-based wearable solutions have the potential for continuous and unobtrusive monitoring. The precise estimation of joint angles is important in applications such as the prevention of osteoarthritis or in the assessment of the progress of physical rehabilitation. We propose a textile-based wearable device for knee angle estimation through capacitive sensors placed in different locations above the knee and in contact with the skin. We exploited this modality to enhance the baseline value of the capacitive sensors, hence facilitating readout. Moreover, the sensors are fabricated with only one layer of conductive fabric, which facilitates the design and realization of the wearable device. We observed the capability of our system to predict knee sagittal angle in comparison to gold-standard optical motion capture during knee flexion from a seated position and squats: the results showed an R 2 coefficient between 0.77 and 0.99, root mean squared errors between 4.15 and 12.19 degrees, and mean absolute errors between 3.28 and 10.34 degrees. Squat movements generally yielded more accurate predictions than knee flexion from a seated position. The combination of the data from multiple sensors resulted in R 2 coefficient values of 0.88 or higher. This preliminary work demonstrates the feasibility of the presented system. Future work should include more participants to further assess the accuracy and repeatability in the presence of larger interpersonal variability.

Wearable 2.0 research has been conducted on the manufacture of smart fitness wear that collects bio-signals through the wearing of a textile-based electrode. Among them, the electromyography (EMG) suit measures the electrical signals generated by the muscles to check their activity, such as contraction and relaxation. General gel-type electrodes have been reported to cause skin diseases due to an uncomfortable feel and skin irritation when attached to the skin for a long time. Dry electrodes of various materials are being developed to solve this problem. Previous research has reported EMG detectio performance and conducted economic comparisons according to the size and shape of the embroidery electrode. On the other hand, these embroidery electrodes still have foreign body sensations. In this study, a moss sEMG electrode was produced with various shapes (W3 and WF) and loop lengths (1-5 mm). The optimized conditions of the embroidery-based electrodes were derived and analyzed with the tactile comfort factors and sensing performances. As the loop length of the electrode increased, MIU and Qmax increased, but the SMD decreased due to the free movement of the threads constituting the loop. Impedance and sEMG detection performance showed different trends depending on the electrode type.

The measurement of respiratory volume based on upper body movements by means of a smart shirt is increasingly requested in medical applications. This research used upper body surface motions obtained by a motion capture system, and two regression methods to determine the optimal selection and placement of sensors on a smart shirt to recover respiratory parameters from benchmark spirometry values. The results of the two regression methods (Ridge regression and the least absolute shrinkage and selection operator (Lasso)) were compared. This work shows that the Lasso method offers advantages compared to the Ridge regression, as it provides sparse solutions and is more robust to outliers. However, both methods can be used in this application since they lead to a similar sensor subset with lower computational demand (from exponential effort for full exhaustive search down to the order of O ( n 2 )). A smart shirt for respiratory volume estimation could replace spirometry in some cases and would allow for a more convenient measurement of respiratory parameters in home care or hospital settings.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.

The interest in wearable devices has expanded to measurement devices for building IoT-based mobile healthcare systems and sensing bio-signal data through clothing. Surface electromyography, called sEMG, is one of the most popular bio-signals that can be applied to health monitoring systems. In general, gel-based (Ag/AgCl) electrodes are mainly used, but there are problems, such as skin irritation due to long-time wearing, deterioration of adhesion to the skin due to moisture or sweat, and low applicability to clothes. Hence, research on dry electrodes as a replacement is increasing. Accordingly, in this study, a textile-based electrode was produced with a range of electrode shapes, and areas were embroidered with conductive yarn using an embroidery technique in the clothing manufacturing process. The electrode was applied to EMG smart clothing for fitness, and the EMG signal detection performance was analyzed. The electrode shape was manufactured using the circle and wave type. The wave-type electrode was more morphologically stable than the circle-type electrode by up to 30% strain, and the electrode shape was maintained as the embroidered area increased. Skin-electrode impedance analysis confirmed that the embroidered area with conductive yarn affected the skin contact area, and the impedance decreased with increasing area. For sEMG performance analysis, the rectus femoris was selected as a target muscle, and the sEMG parameters were analyzed. The wave-type sample showed higher EMG signal strength than the circle-type. In particular, the electrode with three lines showed better performance than the fill-type electrode. These performances operated without noise, even with a commercial device. Therefore, it is expected to be applicable to the manufacture of electromyography smart clothing based on embroidered electrodes in the future.

Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users' acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors.

Recent achievements in the field of miniaturization of electronics have led to a significant interest in its application into the protective clothing for firefighters in order to improve their safety and health. However, up to now there are not any requirements and standardized test methods enabling confirmation of safety of use and functionality of these systems in work environment. In the paper, an importance of evaluation of such smart wearable system in predicted utility conditions is highlighted. Three variants of the specially developed personal warning systems (PWSs) for integration with either health status or environmental sensors to be used in smart protective clothing for firefighters are presented, i.e., with LCD display, LED diodes and vibrating element. For the purpose of evaluation a new testing methodology was developed. The PWSs were evaluated on a basis of perception of warning signals by firefighters in simulated utility conditions including temperature, light, noise, fog and performed activities. In the case of marching, even 75% of signals generated by PWS with LCD display were not received. Physical activity did not influence on functionality of PWS with vibrating element. For the PWS with LED diodes, the signal was received statistically significantly quicker than in the case of other PWS and the mean value of voice response time was below 3 s.

The article presents a methodology developed for the evaluation of biomechanical energy harvesting systems that permits avoiding long-duration outdoor tests while providing realistic input signals and preserving uniform conditions across repeated tests. It consists of two stages: transducer output signal recording and power conversion and storage system measurements. The proposed approach was applied to assess the usefulness of a commercial electromagnetic transducer for supplying a Global Positioning System (GPS) receiver used as an active component of a smart clothing dedicated for mountain rescuers. Electrical power yield measurements have been presented together with ergonomic tests results. They all involved diverse physical activities performed by mountain rescuers that simulated their true operations, but were conducted in a training room for the sake of standardization. By providing reliable data on the transducer's performance under realistic use conditions, the proposed evaluation procedure revealed that the true energy yield was much smaller not only with respect to the manufacturer's assertions, but also substantially lower than what was expected based on an independent review which used unrealistic and non-uniform excitations. On the other hand, ergonomics ratings given by potential end users were very high, which demonstrates that the evaluated transducer can still be useful for supplying active cloth components with a lower power demand. The study also revealed that transducer location and orientation strongly affect its performance, which must be taken into account at the first stage of the evaluation procedure. Moreover, physical activity type and conditions (such as motion speed and ground tilt) influence the output power and should be carefully considered when composing a typical case scenario for the second stage.

Aging women usually experience menopause and currently there is no single diagnosing highly-sensitive and -specific test for recognizing menopause. For most employed women at their perimenopause age it is not convenient to visit a clinic for the hormone test, which lasts for consecutive days. This paper develops a suit of sensor-based smart clothing used for home-based and ambulatory health monitoring for women's menopause transition. Firstly, a survey analysis is conducted to determine the biological signals measured by sensors for indicating the symptoms of menopausal transition and also the body areas with salient symptoms to implant the sensors on the clothing. Then, the smart clothing is designed with a set of temperature and relative humidity sensors on different locations and with a microcontroller to transmit the measured data to the computer. With the smoothed data as input, a new detection algorithm for hot flashes is proposed by recognition of the concurrent occurrence of heat and sweating rise/down, and can figure out the frequency, intensity, and duration-triple dimension information of a hot flash, which is helpful to achieve precise diagnosis for menopausal transition. The smart clothing and the detection algorithm are verified by involving a group of women subjects to participate in a hot flash monitoring experiment. The experimental results show that this smart clothing monitoring system can effectively measure the skin temperature and relative humidity data and work out the frequency, duration, and intensity information of a hot flash pertaining in different body areas for individuals, which are accordant with the practice reported by the subjects., Competing Interests: The authors declare no conflict of interest.

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

The Internet of Things (IoT) scenario is strongly related with the advance of the development of wireless sensor networks (WSN) and radio frequency identification (RFID) systems. Additionally, in the WSN context, for a continuous feed, the integration of textile antennas for energy harvesting into smart clothing is a particularly interesting solution when the replacement of batteries is not easy to practice, such as in wearable devices. This paper presents the E-Caption: Smart and Sustainable Coat. It has an embedded dual-band textile antenna for electromagnetic energy harvesting, operating at global system for mobile communication (GSM) 900 and digital cellular system (DCS) 1800 bands. This printed antenna is fully integrated, as its dielectric is the textile material composing the coat itself. The E-Caption illustrates the innovative concept of textile antennas that can be manipulated as simple emblems. Seven prototypes of these "emblem" antennas, manufactured by lamination and embroidering techniques are also presented. It is shown that the orientation of the conductive fabric does not influence the performance of the antenna. It is also shown that the direction and number of the stitches in the embroidery may influence the performance of the antenna. Moreover, the comparison of results obtained before and after the integration of the antenna into cloth shows the integration does not affect the behavior of the antenna.