The Effect of Sleeve Pattern and Fit on E-Textile Electromyography (EMG) Electrode Performance in Smart Clothing Design.

When e-textile EMG electrodes are integrated into clothing, the fit of the clothing on the body, and therefore its pattern and cut become important factors affecting the EMG signal quality in relation to the seamless contact between the skin and the e-textile electrode. The research so far on these effects was conducted on commercially available clothing or in tubular sleeve forms for arms. There is no study that investigated different clothing pattern and fit conditions and their effect on e-textile EMG electrode performance. This study investigates the effect of clothing pattern and fit in EMG applications using e-textile electrodes integrated onto the sleeves of custom drafted t-shirts in set-in and raglan sleeve pattern variations. E-textile electrode resistance, signal-to-noise ratio (SNRdB), power spectral density and electrode-skin impedance are measured and evaluated in set-in sleeve and raglan sleeve conditions with participants during a standardized arm movement protocol in comparison to the conventional hydrogel Ag/AgCl electrodes. The raglan sleeve pattern, widely used in athletic wear to provide extra ease for the movement of the shoulder joint, showed superior performance and therefore indicated the pattern and cut of a garment could have significant effect on EMG signal quality in designing smart clothing.

Assessing the female figure identification technique's reliability as a body shape classification system.

This paper demonstrates the effects of slight differences in measurement definitions on resultant body shape classification. Ergonomic researchers consider the Female Figure Identification Technique (FFIT) a 'gold standard' body shape classification system to describe variation in a population's 3 D profile. Nevertheless, researchers use FFIT without a scientific basis or considering their ergonomic suitability. This paper rigorously evaluates FFIT, focussing on ergonomics, garment construction, and scientific research applications. Through analysing 1,679 3 D Body Scans, we assess the level of agreement between the FFIT's body shape classification when measurements placed following FFIT's or SizeUK's guidance. We establish how different interpretations of FFIT's measurement placement cause the same body to be categorised into different shapes - in up to 40% of cases. FFIT omits shoulder measurements that have little relationship to body shape yet are vital in garment construction. Using FFIT with different datasets and definitions, therefore, leads to inconsistent conclusions about shape differences.

Digital-Based Healthy Bra Top Design That Promotes the Physical Activity of New Senior Women by Applying an Optimal Pressure.

A bra use can reduce physiological and physical functions because of clothing pressure, which can be a problem for new senior women starting to lose physical function. The present study presents a bra top design development method for promoting new senior women's physical activity by identifying problems related to bras' effects on women's health and minimizing clothing pressure. The analysis utilized the 3D scan data of 42 adult women (age range: 50s) from the 5th Size Korea Project. Bra top design elements were extracted based on new senior consumers' needs. We developed an average wireframe reflecting the new senior's physical characteristics, and a standard body form was developed through surface modeling. To produce a consumer-oriented bra with a body shaping effect and reduced clothing pressure that would not affect physical activities, a three-dimensional pattern was developed applying an optimal reduction rate of 80%. To verify the bra's adequacy for the body form of new senior women, two market-available bras were selected and fit-compared to the developed product. The developed bra received higher expert appearance evaluation and 3D virtual clothing evaluation scores. This study is significant because by using virtual fitting technology, it provides foundational data to quantify the quality of fashion products.

Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

A dataset of a fit assessment study in children is presented. Anthropometric measurements of 113 children were obtained using a 3D body scanner. Children tested a t-shirt of different sizes and a different model for boys and girls, and their fit was assessed by an expert. This expert labeled the fit as 0 (correct), -1 (if the garment was small for that child), or 1 (if the garment was large for that child) in an ordered factor called Size-fit. Moreover, the fit was numerically assessed from 1 (very poor fit) to 10 (perfect fit) in a variable called Expert evaluation. This data set contains the differences between the reference mannequin of the evaluated size and the child׳s anthropometric measurements for 27 variables. Besides these variables, in the data set, we can also find the gender, the size evaluated, and the size recommended by the expert, including if an intermediate, but nonexistent size between two consecutive sizes would have been the right size. In total, there are 232 observations. The analysis of these data can be found in Pierola et al. (2016) [2].