Intermedia refers to the convergence of media. The advance of intermedia has not only facilitated the delivery of brand messages in contemporary fashion shows but also facilitated interactive communication. This study investigated the mediating roles played by various media in fashion and fashion shows, focusing on the phenomenon of intermedia in contemporary fashion shows. To investigate the impact of intermedia on contemporary fashion shows, we conducted a social network analysis—a promising approach for research into fashion trends. Analyzing 159 fashion-related articles published in the 2000s, we extracted intermedia-related words (n=253). The relationships between keywords made an analysis of between centrality, and cluster variables applied Clauset-Newman-Moore by using KrKwic and NodeXL programs. The results of the between centrality analysis indicated that the most important factors in contemporary fashion shows are “models” and “stages.” We found that the impacts of intermedia on contemporary fashion shows can be divided into four categories: “model performance,” “symbolic stage management,” “new media utilization,” and “convergence in arts.” Our analysis thus identified considerable synergy between the characteristics of intermedia and contemporary fashion shows. These results have found intermediarelated commonalities in intermedia and fashion show, and this might increase customer interest in fashion, a positive outcome for the fashion industry.

Intermedia is a term that refers to the convergence of different media. It has not only enabled the delivery of brand message but also developed interactive communication in a contemporary fashion show context. This study investigates the presentation methods of contemporary fashion shows affected by intermedia. We examined the research and theory related to intermedia and fashion shows by accessing articles since the 2000s concerning 252 shows by 30 designers that tended to demonstrate the use of intermedia. Our analysis highlighted four particular characteristics: “medium scalability” (the unity of art and fashion and the unity of life and fashion); “synesthetic flow” (the audience’s emotional immersion, represented by the actual performance and performances of the models); “participatory accessibility” (performance participation through action and instrumental support); and “immaterial symbolization” (an intuitive symbol that emphasizes both the direct outward appearance and the metaphorical symbolism so that the object is expressed in a figurative sense). From these results, we identified considerable synergy between the characteristics of intermedia and contemporary fashion shows. Therefore, it might be useful to conduct an experimental study on this area of research.

The aim of this paper is to examine relationship characteristics and the relevance there of in the context of the Chanel collection, between the contemporary fashion show viewed from the artistic side and intermedia on interaction. This study analyzed the relationship characteristics between intermedia and fashion shows by focusing on Chanel fashion shows from 2010 S/S to 2016 F/W as actual case. The results are as follow: First, participational accessibility indicates the experience of a designer and audience in active participation changing into passive participation. Second, audience immersion is represented by the perception of being involved by the audience in the place and acting. Third, conceptual symbolism is sending of messages through the designer’s abstract concept manifesting in the specific features of the design. Finally, expandability of media explains trans-boundaries in experimental attempts. It concludes that fashion show is a space where the audience can have visual as well as new knowledge and experience through multi-sensory channels. The Chanel collection which is considered a huge art project at the fashion show, showed a relationship between philosophy and the direction through season concepts and brands. The Chanel collection has brought a new experience through the convergence of communications and cultural media, such as music and art according to traditional brand image.

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform ‘tying up shoe’ using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user’s intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

The paper proposes a stiffness measurement device composed of a measurement part including six indenters and a fixing part including four fixtures. The device is able to make simultaneously measurements of the stiffness of human arm. The six indenters make use of both position and force control schemes sequentially whenever needed. In addition, the loadcells and the digital encoders are attached to the indenters and electric motors, respectively, so that the data can be provided in real time. On the end of the indenter, two-axis potentiometer is attached in order to measure the angle difference between the applied force axis and the axis normal to the skin of human arm, and to convert the force measured on the loadcell into the actual applied force to skin. For this purpose, the mapping between the voltage output and the angle of potentiometer was obtained by fitting it for each axis. Ultimately, the measurement device was able to measure the stiffnesses of six regions of human arm.

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).