Industrial applications of affective engineering /
edited by Junzo Watada, Hisao Shiizuka, Kun-Pyo Lee, Tsuyoshi Otani, Chee-Peng Lim.
- xi, 296 páginas : 170 ilustraciones, 114 ilustraciones en color.
Springer eBooks
A Bio-signal-based Control Approach to Building a Comfortable Space -- A New Social Emotion Estimating Method by Measuring Micro Movement of Human Bust -- Affective Engineering in Applications to Bilevel Human Migration Models -- Analysis and Evaluation of Business Signs Using Deviation Values -- Defining Design Subjects according to the Context in which Problems Occur -- Design Management Strategy - A Case Study of an Affective Product -- Kansei as a Function of Aesthetic Experience in Product Design -- Kawaii Rules: Increasing Affective Value of Industrial Products -- Modeling Emotional Evaluation of Traditional Vietnamese Aodai Clothes Based on Computer Vision and Machine Learning -- Near-Infrared Spectroscopy (NIRS) Analysis of Emotion when Reading e-Books with Sound Effects -- The Effects of Culture on Users’ Perception of a Webpage: A Comparative Study of the Cognitive Styles of Chinese, Koreans and Americans -- Backrest Designs in Meeting Chairs -- Branding Luxury through Affective Value - Case of Swiss Watch Industry -- Button Sound Quality Evaluation for Car Audio Main Units -- Characteristics of the Design and Production Process for Italian and Japanese Made Tailored Jackets in the Global Market -- Online Shopping and Individual Consumer Adaptation: The Relationship between Fabric-identification Ability and Prior Knowledge -- Reading Emotion of Color Environments: Computer Simulations With Self-Reports and Physiological Signals -- Reviewing the Role of the Science Fiction Special Interest Group via User Interfaces: the Case of Science Fiction Movies -- Sleep Quality and Skin-lightening Effects of White Mother Chrysanthemum Aroma -- The Emotional Characteristics of White for Applications of Product Color Design -- The Influence of Skincare Routines on Skin Physiology Parameters and Affective-Values -- Understanding Product Features Using a Hybrid Machine Learning Model.