From Sensory Analysis to Human Centered Design

1. From Sensory Analysis to Human Centered Design By Xianyi Zeng ENSAIT Textile Institute, Roubaix, France

2. ENSAIT : Roubaix city Lille Urban Community

3. Histroy of the ENSAIT 1889 Creation of the School of Fine Arts. 1921 The Creation of the ENSAIT. 1945 Creation of the ENSAIT Engineering Degree.(Masters Degree) 1992 - 2001 Development of the school. - Research - International dimension - Relations with industry

4. GEMTEX Research Laboratory

5. HCD Research Team Social and economic context in textile industry • - International competitions and delocalization • Diversified markets and demanding consumer’s • requirements: comfort, health, well-being • Challenges and opportunities with Internet: • 30-40% of European garment sales realized on Internet • Environmental disasters and hazards • 18% of toxic chemicals in China from textile industry • fast fashion waste: 20% of growing rate in Europe • Energy consumption: 10% of the world productive energy • Social impacts: unemployment in consumer countries, • labor exploitation in producer countries

6. HCD Research Team Social and economic context in textile industry • According to EMCC (European Monitoring Center on Change) • a new textile and fashion development model • Sustainable fashion: eco-materials, environment protection, society impact and human • factors • Fashion industry: from resource based industry to knowledge-centered industry • Co-design: all people bring ideals and perspectives to the design process • Existing European projects • LEAPFROG – FP6: • optimized manufacturing, mass customization • ECOWATER – FP7: • eco-efficiency indicators for technology assessment

7. HCD Research Team Research Program • Computerized multifunctional material design • Sensory and emotion design – virtual prototyping – integration – virtual fitting room design – mass customization • Intelligent clothing design – sensor and actuator integration – advanced signal processing - decision support system • Supply chain design and optimization – mass customization – mini factory – LCA – sustainable development – decision support system

8. HCD Research Team Research staff in 2011/2012 • Permanent researchers/teachers: • 9 teachers from ENSAIT • 3 teachers from HEI • PhD students : • 11 PhD students carrying out their projects • 6 PhD students defended their thesis

9. HCD Research Team Research results in HCD ( since 2009) • - Journal papers with IF>0.5: 50 • Chapters in scientific books: 8 • Patents: 6 • Industrial contracts (>10): • France Télécom, Unilever, Decathlon, Damart, Bureau Veritas … • European and national projects: • INTERREG RESIST, ERASMUS MUNDUS, 2 projects on 7th FP, • 2 project ANR, 3 projects FUI, …

10. HCD Research Team Computerized design of advanced materials using decision support systems Computer Aided Design and simulation Production line Modeling of materials and processes by combing expert knowledge and experimental data

11. HCD Research Team New concept for garment design and production, man/garment interface 1 2 3 5 7 6 4 8 Personalized virtual prototyping based design process • - Human body modeling from body scanner’s data using a neural network • - Building a 3D adaptive and parametric model based on Madaris 3D Fit • Comfort integration by modeling of ease allowance and sensory analysis

12. HCD Research Team Sustainable development-based design for materials, processes and supply chain - LCA – Decision support for selecting relevant materials and suppliers - Forecasting of consumer needs – experience feedback – zero stock - Fastfashion – new production system modeling and simulation

13. HCD Research Team Sensory design: modeling of designer and consumer perception Design space Perception space Production space Production parameters: machine parameters, material parameters, … Physical features: mechanical properties, thermal properties, … Complex concepts: comfort, fashion style Evaluation indicators or ambiances: relaxation, sport, … Perception in a social context Tactile descriptors: soft, smooth, … Audio descriptors Visual descriptors : bright, dark color, … Basic perception on products

14. HCD Research Team Design of smart textiles • Textile sensors and actuators • Flexible displays • Textile electronics

15. HCD Research Team Intelligent clothing system with decision support Psychological and physiological signal analysis and characterization for different scenarios : - Driving cars - medical diagnosis - Purchasing - psychological/emotional analysis

16. Human Centered Design Human perception related projects: 1) Textile sensory and emotion design: from real human perception criteria (fabric hand, color, well-being) to textile design parameters 2) Personalized garment co-design platform: from virtual perception to textile design parameters/fashion recommender system

17. Textile Sensory Design • Components of textile comfort • - Thermal comfort: heat and moisture transfers • Tactile comfort: fabric hand • Visual comfort: textile appearance (color, texture) • Fitting comfort: cut, garment making, … • Acoustic comfort • Odor comfort

18. Textile Sensory Design • Textile comfort by sensory evaluation and modeling Applications fabric hand, thermal comfort, color, cosmetic

19. Fabric Hand Evaluation – Psychological Study Sensory evaluation • Primary Hand: description of textile surface properties using • linguistic expressions (soft, smooth, dry, fresh, …) • Total Hand: describing general quality of fabrics related to • specific applications

20. Fabric Hand Evaluation – Psychological Study Sensory evaluation (scores given by Experts and trained panel)

21. Fabric Hand Evaluation – Physical Study • Objective evaluation • - Simulation of human hand • Measuring texture of textile surface: UST (visco-elasticity), optical 3D-profiler (surface topography) • Measuring mechanical and thermal properties: • KAWABATA or KES (shearing, bending, friction, • compression, tensile, …) • FAST (extensibility, compression, bending) • Multidirectionnel tribometer • Optical roughness meter, calorimeter, drape, …

22. Fabric Hand Evaluation – Physical Study KES – Kawabata Evaluation System tensile compression bending surface

23. Fabric Hand Evaluation – Modeling Model#1: physical features interpretation 1) selecting relevant physical features 2) modeling the relationship between physical features and sensory attributes 3) optimizing structure and parameters of the models Complex system Physical features (input variables) Sensory descriptors (output variables) Used techniques Soft computing, integration of human knowledge and data

24. Fabric Hand Evaluation – Modeling Model#2: characterizing the relation between fabric process parameters and selected physical features - input variables - 5 process parameters: 2 linguistic variables (quality, process type) 3 numerical variables (count, twist, cover factor) - output variable: each selected physical feature - principle: cross-validation and combination of numerical data and human knowledge

25. Fabric Hand Evaluation • Other work • Computing similarity between sensory panels (B2B) • Modeling relations between consumers’ preference and • sensory evaluation given by trained panels (B2C) • 3) Analysis and classification of consumers according to their • fabric hand evaluation data • 4) Analysis of effectiveness of sensory descriptors

26. Fitting Comfort Principles 1) Selecting relevant body measurements 2) Formalization of fitting comfort 3) Determination of comfort oriented garment patterns

27. Fitting Comfort Adjustable trousers Data acquisition: 1) Taking body measurements 2) Evaluating comfort levels at different postures Relevant body measurements related to the gluteal region and the trouser of normal size

28. Fitting Comfort Comparison between fuzzy pattern (blue line) and classical pattern (red line)

29. Modeling for well-being • Modeling the relation between design parameters and criteria of well-being • Evaluation of consumer’s perception on well-being • Fuzzy model and learning from data • Selecting the most relevant materials (fabric hand) • Selecting the most relevant styles and colors • Global evaluation of well-being

30. Modeling for well-being

31. Evaluation of textile materials in terms of well-being Weights: - AI: Absolutely Important - SI: Strongly Important - I: Important Well-Being of Textiles Attributes: Attributes: - Washing care - Ironing - Storage - Washing care - Ironing - Storage - Washing care - Ironing - Storage

32. Garment Co-Design Platform From e-shopping to personalized garment design • Current garment e-shopping: • E-catalogs based on classification • Visualization: photos (mostly) and 3D virtual objects • Virtual perception: visual effects only, static fitting

33. Garment Co-Design Platform From e-shopping to personalized garment design • E-shopping in the future: • - More complete virtual environment => • Personalized fitting • Virtual perception: visual effects (static and dynamic) + fabric hand + comfort + controlled virtual ambiance • Virtual sales advisor => design knowledge management + Searching engine: from consumer’s needs to garments • - A cooperative garment design platform => • Interactions between consumer, designer and material developer • Searching engine: from consumer’s needs to new technical parameters (specialized patterns, fabrics, colors, textures, …) • Consumer behavior characterization => forecasting • Customized production planning and supply chain planning with online tracking

34. Garment Co-Design Platform New concept for garment design and production, man/garment interface 1 2 3 5 7 6 4 8 Personalized virtual prototyping based design process • - Human body modeling from body scanner’s data • - Building a 3D adaptive and parametric model • Comfort integration by modeling of ease allowance and sensory analysis

35. Project 1: predicting design parameters from perception Garment co-design – modeling by learning from data Mechanical properties Visual/tactile perception Optical properties Model Patterns • Modeling from data for controlling perception of virtual garments • Samples preparation – fabric selection, virtual garment making • Measurement of mechanical and optical properties for all the samples • Sensory evaluation for the virtual and real garments • Optimization of visual and tactile effects of the virtual garments

36. Project 1: predicting design parameters from perception Visualization of one real and two virtual flared skirts Real sample Virtual sample generated by the CAD software – quite different from the real one Virtual sample modified by the model – mechanical properties closer to the real one

37. Project 2: vision/tactile interaction Background Virtual Experience Reliable sense of fabric • Haptic force feedback device PHATom (TiNi Alloy. US) Cyberglove (Virtual technologies.inc. US)

38. Project 2: vision/tactile interaction Background Multi-channel neuropsychological perception model: Visual information Relational memory Tactile information STORE our study: Interpretability Visual representation Tactile Properties Interactive mechanism

39. Project 2: vision/tactile interaction Proposed algorithm: Inclusion Degree (Rough-Fuzzy) Classification consistency: Visual assessment General consistency Aggregation criterion Fuzzy inference system Real assessment Ranking consistency: Kendall’s coefficient

40. Project 2: vision/tactile interaction • Experiment 1 (Fabric hand evaluation) Video scenario Real-sample scenario Image scenario • Descriptors: • Scales

41. Project 2: vision/tactile interaction Sensory experiment 1 • Results and discussion Internal texture Surface attribute Tensile and shearing Bending Structural Parametric Compression GCons Descriptor

42. Project 2: vision/tactile interaction Image + video => Virtual prototype - Interpret tactile information from virtual prototype - Adjust model parameters according to the desired tactile property in order to design the most appropriate material Tactile information

43. Project 3: personalized recommender system Body Shape Classification: perception-based Human Body Shapes Level 1: More concrete and more related to the basic nature of body shapes, independent of socio-cultural context Sensory Descriptors Ex. Thin - Fat Level 2: More abstract and complex, strongly related to the socio-cultural context Fashion Themes Ex. Sporty

44. Project 3: personalized recommender system Recommender System: evaluating a new style relative to a specific body shape and an expected fashion theme Output of Model I Output of Model II Relevancy (BR, T) Relevancy (BR_de, T) whether a garment design is feasible for a specific body shape in terms of promotion of its relevancy to the fashion theme ?

45. Several considerations on cooperation 1) Virtual human body modeling with photos 2) Virtual garment fitting process with designer’s knowledge 3) Designer’s knowledge formalization, extraction and modeling 4) A fabric material library with knowledge 5) Automatic extraction of visual and tactile sensory descriptors from images and videos