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  • 1.
    Jain, Sheenam
    et al.
    University of Borås, Faculty of Textiles, Engineering and Business. Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France; College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China; Automatique, Génie informatique, Traitement du Signal et des Images, Université Lille Nord de France, F-59000 Lille, France.
    Giri, Chandadevi
    University of Borås, Faculty of Textiles, Engineering and Business. Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France; College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China; Automatique, Génie informatique, Traitement du Signal et des Images, Université Lille Nord de France, F-59000 Lille, France.
    Zeng, Xianyi
    Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France.
    Bruniaux, Pascal
    Laboratoire de Génie et Matériaux Textiles (GEMTEX), ENSAIT, F-59000 Lille, France.
    A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry2019In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed)
    Abstract [en]

    The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ‘‘Scopus’’ and ‘‘Web of Science’’ and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.

  • 2.
    Vega-Barbas, Mario
    et al.
    Royal Institute of Technology.
    Pau, Iván
    Universidad Politecnica de Madrid.
    Augusto, Juan C
    Middlesex University.
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. University of Borås, Faculty of Caring Science, Work Life and Social Welfare. Karolinska Institutet.
    Interaction patterns for smart spaces. A confident interaction design solution for pervasive sensitive IoT services2017In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed)
  • 3.
    Wollmann, Thomas
    et al.
    GECKO Institute, Heilbronn University, Heilbronn, Germany.
    Abtahi, Farhad
    KTH-School of Technology and Health.
    Eghdam, Abouzar
    Department of Learning, Informatics, Management and Ethics, Health Informatics Centre, Karolinska Institute.
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. KTH-School of Technology and Health.
    Lindecrantz, Kaj
    KTH-School of Technology and Health.
    Haag, Martin
    GECKO Institute, Heilbronn University, Heilbronn, Germany.
    Koch, Sabine
    Department of Learning, Informatics, Management and Ethics, Health Informatics Centre, Karolinska Institute.
    User-Centred Design and Usability Evaluation of a Heart Rate Variability Biofeedback Game2016In: IEEE Access, E-ISSN 2169-3536, Vol. PP, no 99, p. 1-1Article in journal (Refereed)
    Abstract [en]

    Background and objective: Reduced heart rate variability (HRV) is an indicatorof a malfunctioning autonomic nervous system. Resonant frequencybreathing is a potential non-invasive means of intervention for improvingthe balance of the autonomic nervous system and increasing HRV. However,such breathing exercises are regarded as boring and monotonous tasks.The use of gaming elements (gamification) or a full gaming experience is awell-recognised method for achieving higher motivation and engagement invarious tasks. However, there is limited documented knowledge on how todesign a game for breathing exercises. In particular, the influence of additionalinteractive elements on the main course of training has not yet beenexplored. In this study, we evaluated the satisfaction levels achieved usingdifferent game elements and how disruptive they were to the main task, i.e.,paced breathing training.

    Methods: An Android flight game was developed with three game modes thatdiffer in the degrees of multitasking they require. Design, development and evaluation were conducted using a user-centred approach, including contextanalysis, the design of game principle mock-ups, the selection of game principlesthrough a survey, the design of the game mechanics and GUI mock-up,icon testing and the performance of a summative study through user questionnairesand interviews. A summative evaluation of the developed gamewas performed with 11 healthy participants (ages 40-67) in a controlled setting.Results: The results confirm the potential of video games for motivatingplayers to engage in HRV biofeedback training. The highest training performanceon the first try was achieved through pure visualisation rather thanin a multitasking mode. Players had higher motivation to play the morechallenging game and were more interested in long-term engagement.Conclusion: A framework for gamified HRV biofeedback research is presented.It has been shown that multitasking has considerable influence onHRV biofeedback and should be used with an adaptive challenge level.

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