ANFIS modeling for predicting affective responses to tactile textures

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to simulate and analyze the mapping between the physical properties of tactile textures and people's affective responses. People were asked to rate the tactile feeling of 37 tactile textures against six pairs of adjectives on a semantic differential questionnaire. The friction coefficient, average roughness, compliance, and a thermal parameter of each tactile texture were measured. ANFIS models were built to predict the affective responses to tactile textures. The resulting ANFIS models demonstrated a good match between predicted and actual responses, and always yielded better performance when compared to linear and exponential regression models. The effects of physical properties of textures on affective responses were also analyzed by simulating the synthetic data with ANFIS. © 2011 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

[1]  Mark Hollins,et al.  The coding of roughness. , 2007, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[2]  Tomio Jindo,et al.  A fuzzy logic analysis method for evaluating human sensitivities , 1995 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[4]  Brian Henson,et al.  Surface finish and touch: a case study in a new human factors tribology , 2004 .

[5]  Li Pheng Khoo,et al.  A dominance-based rough set approach to Kansei Engineering in product development , 2009, Expert Syst. Appl..

[6]  E. Hirschman,et al.  The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun , 1982 .

[7]  A. Fahri Ozok,et al.  A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system , 2003 .

[8]  Takehisa Onisawa,et al.  Soft Computing Technique in Kansei (Emotional) Information Processing , 2000 .

[9]  Derek G. Chetwynd,et al.  Quantifying touch–feel perception: tribological aspects , 2008 .

[10]  H. Kärkkäinen,et al.  Ten tools for customer-driven product development in industrial companies , 2001 .

[11]  Diyar Akay,et al.  A neuro-fuzzy based approach to affective design , 2008 .

[12]  Masafumi Hagiwara,et al.  A Fuzzy Rule Based Personal Kansei Modeling System , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[13]  Hideyoshi Yanagisawa,et al.  Interactive Reduct Evolutional Computation for Aesthetic Design , 2005, J. Comput. Inf. Sci. Eng..

[14]  Kyungmee Choi,et al.  A systematic approach to the Kansei factors of tactile sense regarding the surface roughness. , 2007, Applied ergonomics.

[15]  Shih-Wen Hsiao,et al.  A neural network based approach for product form design , 2002 .

[16]  Chung-Hsing Yeh,et al.  User-oriented design for the optimal combination on product design , 2006 .

[17]  S. Han,et al.  A systematic approach for coupling user satisfaction with product design , 2003, Ergonomics.

[18]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[19]  David Jobber,et al.  Competing through design , 1998 .

[20]  Halimahtun M Khalid,et al.  Embracing diversity in user needs for affective design. , 2006, Applied ergonomics.

[21]  Halimahtun M. Khalid,et al.  Customer Emotional Needs in Product Design , 2006, Concurr. Eng. Res. Appl..

[22]  Brian Henson,et al.  Affective Consumer Requirements: A Case Study of Moisturizer Packaging , 2006, Concurr. Eng. Res. Appl..

[23]  Roger Jianxin Jiao,et al.  A Kansei mining system for affective design , 2006, Expert Syst. Appl..

[24]  Jérome Pailhes,et al.  Identification of sensory variables towards the integration of user requirements into preliminary design , 2007 .

[25]  Cheol Lee,et al.  A Statistical Model of Relationship Between Affective Responses and Product Design Attributes for Capturing User Needs , 2007, HCI.

[26]  Sung H. Han,et al.  A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design , 2004 .

[27]  Pei-Chann Chang,et al.  A fuzzy case-based reasoning model for sales forecasting in print circuit board industries , 2008, Expert Syst. Appl..

[28]  Shang H. Hsu,et al.  A semantic differential study of designers’ and users’ product form perception , 2000 .

[29]  Hsin-Hsi Lai,et al.  A robust design approach for enhancing the feeling quality of a product: a car profile case study , 2005 .

[30]  Yukihiro Matsubara,et al.  An analysis of Kansei structure on shoes using self-organizing neural networks , 1997 .

[31]  Mitsuo Nagamachi,et al.  Kansei engineering: A study on perception of vehicle interior image , 1997 .

[32]  B Henson,et al.  Human tactile perception of screen-printed surfaces: Self-report and contact mechanics experiments , 2007 .

[33]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[34]  Li Pheng Khoo,et al.  An investigation into affective design using sorting technique and Kohonen self-organising map , 2006, Adv. Eng. Softw..

[35]  Mitsuo Nagamachi,et al.  Kansei engineering and application of the rough sets model , 2006 .

[36]  Myung Hwan Yun,et al.  Identifying mobile phone design features critical to user satisfaction , 2004 .

[37]  Mitsuo Nagamachi Kansei Engineering and Rough Sets Model , 2006, RSCTC.

[38]  Chung-Hsing Yeh,et al.  Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones , 2007 .

[39]  P. Hekkert,et al.  Meanings of materials through sensorial properties and manufacturing processes , 2009 .

[40]  Abdulsamet Haşiloğlu Rotation-Invariant Texture Analysis and Classification by Artificial Neural Networks and Wavelet Transform , 2001 .

[41]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[42]  Chung-Hsing Yeh,et al.  Form design of product image using grey relational analysis and neural network models , 2005, Comput. Oper. Res..

[43]  J. Russell Core affect and the psychological construction of emotion. , 2003, Psychological review.

[44]  P. Jordan Designing Pleasurable Products: An Introduction to the New Human Factors , 2000 .

[45]  Ming-Chuen Chuang,et al.  Expressing the expected product images in product design of micro-electronic products , 2001 .

[46]  Myung Hwan Yun,et al.  Affective Evaluation of Vehicle Interior Craftsmanship: Systematic Checklists for Touch/Feel Quality of Surface-Covering Material , 2004 .

[47]  Mitsuo Nagamachi,et al.  ACQUISITION OF KANSEI DECISION RULES OF COFFEE FLAVOR USING ROUGH SET METHOD , 2006 .

[48]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[49]  David Sprott,et al.  The influence of tactile input on the evaluation of retail product offerings , 2007 .

[50]  Bernard Yannou,et al.  Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics , 2004 .

[51]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[52]  Ying-Ming Wang,et al.  An adaptive neuro-fuzzy inference system for bridge risk assessment , 2008, Expert Syst. Appl..