Kidsize: always get the right size! @3DBody.Tech 1st Dec 2016
Report
Share
•1 like•594 views
Kidsize: always get the right size! @3DBody.Tech 1st Dec 2016
•1 like•594 views
Report
Share
Kidsize is a proof-of-concept developed by IBV. This presentation describes the two innovations underpinning Kidsize concept and presents the results of their validation. The first one is a mobile phone app to measure a child in 3D by taking two pictures. This new method is more accurate and consistent than an untrained person using a measuring tape at home or in the shop. The second one is an expert system that recommends the size that best fits the child and assesses the fit of the garment at different body areas. Project results show that it can provide nearly 90% right size recommendations, thus outperforming existing methods like age- or height-based size guides, which achieve 40 and 60% right recommendations respectively.
Recommended
More Related Content
Similar to Kidsize: always get the right size! @3DBody.Tech 1st Dec 2016
![[DigiHealth 22] Artificial intelligence in medicine - Kristijan Saric](https://cdn.slidesharecdn.com/ss_thumbnails/kristijansaric-artificialintelligenceinmedicine-221129213910-27e4c4c2-thumbnail.jpg?width=384&fit=bounds)
Similar to Kidsize: always get the right size! @3DBody.Tech 1st Dec 2016(20)
![[DigiHealth 22] Artificial intelligence in medicine - Kristijan Saric](https://cdn.slidesharecdn.com/ss_thumbnails/kristijansaric-artificialintelligenceinmedicine-221129213910-27e4c4c2-thumbnail.jpg?width=768&fit=bounds)
Recently uploaded
Recently uploaded(20)
Kidsize: always get the right size! @3DBody.Tech 1st Dec 2016
- 1. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Always get the right size! Alfredo Ballester & Ana Piérola
- 2. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland IBV is a private not-for-profit R&D organisation Product design consultancy for manufacturing industries Technology development partner for IT companies and Academia Apparel Sports Transport Health Safety Leisure Appliances Elderly Orthotics Conduction of 3D body scanning surveys Data-driven 3D modelling from scans (3D) images (2D) or body measurements (1D) Data analytics & services (measuring, shape analyses, size advice) Applications & hardware prototyping
- 3. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 4. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 5. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Why kidsize? Ingredients for size advice
- 6. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Proof-of-concept for Inditex Why kidsize? Brand exp. 41% 21% 64% / 55% Size guide 41% 5% 52% / 43% IBV model 86% 93% 83% / 93% SIZE SELECTION MODEL Fitting percention on body parts Size selection FITTING MOLDEL OF BODY PARTS FITTING PROBABILITY OF KEY AREAS CONTRIBUTION OF FITTING AREAS STAGE 2 SIZE RECOMENDED STAGE 1 FITTING PREDICTION ON KEY BODY PARTS Body measurements Garment sizes
- 7. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Proof-of-concept for Inditex Why kidsize? Brand exp. 41% 21% 64% / 55% Size guide 41% 5% 52% / 43% IBV model 86% 93% 83% / 93% SIZE SELECTION MODEL Fitting percention on body parts Size selection FITTING MOLDEL OF BODY PARTS FITTING PROBABILITY OF KEY AREAS CONTRIBUTION OF FITTING AREAS STAGE 2 SIZE RECOMENDED STAGE 1 FITTING PREDICTION ON KEY BODY PARTS Body measurements Garment sizes • Bodies measured with Vitus XXL scanner • All garments and sizes measured by hand • Single garment models trained with fit trials
- 8. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Why kidsize? Co-funded by the EC (2013-2016) Grant Agreement no. 606091 Industry Promoters DemonstratorsDevelopers
- 9. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Key developments body measuring instrument
- 10. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Key developments body measuring instrument Size advice & fit prediction algorithms
- 11. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Key developments body measuring instrument Size advice & fit prediction algorithms
- 12. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland How does it work?
- 13. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 14. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Body measuring app measured in 3D by taking two photographs estimated by linear regression from gender-age-weight-height 0 to 3 y.o. 3 to 12 y.o.
- 15. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Children body shape space • 800 registered 3D body scans of children • Harmonised in posture • PA + PCA to synthesise • Shape & postural body descriptors • Affordable no. of parameters
- 16. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Children body shape space PC1 PC2 PC3 PC60 …. • 800 registered 3D body scans of children • Harmonised in posture • PA + PCA to synthesise • Shape & postural body descriptors • Affordable no. of parameters
- 17. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Input data Image processing Iterative optimisation Data extraction How it works
- 18. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 19. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Size advice algorithms • Size recommended to wear it straight away → Expert’s advice • Best fit to allow room for the child to grow → Parents’ advice • Fit at different body areas → Traffic Lights
- 20. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Size advice algorithms • Size recommended to wear it straight away → Expert’s advice • Best fit to allow room for the child to grow → Parents’ advice • Fit at different body areas → Traffic Lights
- 21. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Size advice algorithms • Size recommended to wear it straight away → Expert’s advice • Best fit to allow room for the child to grow → Parents’ advice • Fit at different body areas → Traffic Lights
- 22. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland 160 children aged 0-12 y.o. Training data NECK OPENING CHEST WAIST CONTOUR HIP CONTOUR SHOULDER WIDTH TOTAL LENGTH ARMHOLE BACK WIDTH Over 1100 fit trials Key body fitting areas WAIST CONTOUR HIP CONTOUR THIGH CONTOUR FRONT RISE KNEE CONTOUR BACK RISE TROUSERS LENGTH TROUSERS CUFF
- 23. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Ordinal Logistic Regression with Stepwise variable selection 𝑙𝑜𝑔𝑖𝑡 𝑃 𝑓𝑖𝑡 ≤ 𝐴|𝑋1, … , 𝑋𝑘 = ln 𝑃 𝑓𝑖𝑡 ≤ 𝐴|𝑋1, … , 𝑋𝑘 1 − 𝑃 𝑓𝑖𝑡 ≤ 𝐴|𝑋1, … , 𝑋𝑘 = 𝜃𝐴 | 𝐴+1 − 𝛽1 𝑋1 + ⋯ + 𝛽𝑘 𝑋𝑘 , 𝐴 ∈ 𝑠𝑚𝑎𝑙𝑙, 𝑔𝑜𝑜𝑑, 𝑙𝑎𝑟𝑔𝑒 ∶ 𝑠𝑚𝑎𝑙𝑙 ≤ 𝑔𝑜𝑜𝑑 ≤ 𝑙𝑎𝑟𝑔𝑒 𝑃 𝑓𝑖𝑡 = 𝑠𝑚𝑎𝑙𝑙 | 𝑋1, … , 𝑋 𝑘 = 𝑒 𝜃 𝑠𝑚𝑎𝑙𝑙 | 𝑔𝑜𝑜𝑑 − 𝛽1 𝑋1+⋯+𝛽 𝑘 𝑋 𝑘 𝑃 𝑓𝑖𝑡 = 𝑔𝑜𝑜𝑑 | 𝑋1, … , 𝑋 𝑘 = 𝑒 𝜃 𝑔𝑜𝑜𝑑 | 𝑙𝑎𝑟𝑔𝑒 − 𝛽1 𝑋1+⋯+𝛽 𝑘 𝑋 𝑘 − 𝑃 𝑓𝑖𝑡 = 𝑠𝑚𝑎𝑙𝑙 | 𝑋1, … , 𝑋 𝑘 𝑃 𝑓𝑖𝑡 = 𝑙𝑎𝑟𝑔𝑒 | 𝑋1, … , 𝑋 𝑘 = 1 − 𝑃 𝑓𝑖𝑡 = 𝑠𝑚𝑎𝑙𝑙 | 𝑋1, … , 𝑋 𝑘 − 𝑃(𝑓𝑖𝑡 = 𝑔𝑜𝑜𝑑 | 𝑋1, … , 𝑋 𝑘) Fit modelling • Pick biggest size with good fit • If between sizes → Pick larger • If all big or all small → No size available • Consistency expert-parent → Expert Parent Decision rules for picking a size from probabilities
- 24. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Upper bodyLower body Full body Stepwise selection of variables for final models Height Knee heightCervical height Mid neck girthChest girth Waist girth Hip girth Back armpits contour Height Cervical height Hip girth Belly girth Head girth Wrist girth Thigh girth Chest girth Mid neck girth Fit modelling Expert advice Parents advice Fit-by- area
- 25. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 26. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Databases & APIs Add-on & iFrame for e-commerce 3D reconstruction engine Mobile App for parents Size recommendation engine FRONTENDBACKEND Back-offices for producers & e-retailers Prototypes for testing
- 27. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Precision of kidsize app • 6 3D printed children figurines in 1:10 scale • Each figurine measured with the app 10 times • Body measurements digitally obtained from 3D reconstructions • Mean Absolute Difference (MAD) calculated for each measurement 𝑀𝐴𝐷 = 1 𝑛 1 𝑟 𝑖 2 𝑚 𝑠 𝑖 − 𝑚𝑡 𝑖 𝑟 𝑖 𝑡=𝑠+1 𝑟 𝑖−1 𝑠=1𝑖
- 28. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Kidsize Expert 3D scan non- expert Cervical height 3 (0%) 2 3 Knee height 3 (1%) 2 3 Mid neck girth 5 (2%) 3 4 6 Neck base girth 5 (2%) 3 6 Shoulder length 5 (5%) 2 3 Chest girth 8 (1%) 7 7 Back Armpits Contour 6 (2%) 6 7 Waist girth 8 (1%) 5 5 19 Seat girth 6 (1%) 4 5 10 Arm length 6 (1%) 4 7 Outside leg length 5 (1%) 3 5 Thigh girth 5 (1%) 3 2 Knee girth 3 (1%) 3 2 Precision (MAD in mm)
- 29. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Kidsize Expert 3D scan non- expert Cervical height 3 (0%) 2 3 Knee height 3 (1%) 2 3 Mid neck girth 5 (2%) 3 4 6 Neck base girth 5 (2%) 3 6 Shoulder length 5 (5%) 2 3 Chest girth 8 (1%) 7 7 Back Armpits Contour 6 (2%) 6 7 Waist girth 8 (1%) 5 5 19 Seat girth 6 (1%) 4 5 10 Arm length 6 (1%) 4 7 Outside leg length 5 (1%) 3 5 Thigh girth 5 (1%) 3 2 Knee girth 3 (1%) 3 2 Precision (MAD in mm)
- 30. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Accuracy of kidsize app • 34 children aged 3-12 y.o. • Each child was scanned with 3D scanner (Vitus XXL) and with the app • Body measurements digitally obtained from 3D reconstructions • Mean Absolute Difference (MAD) calculated for each measurement 𝑀𝐴𝐷 = 1 𝑛 𝑚 𝐾𝑖𝑑𝑠𝑖𝑧𝑒 𝑖 − 𝑚3𝐷𝑠𝑐𝑎𝑛 𝑖 𝑖
- 31. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Kidsize vs. 3Dscan Expert vs. Expert* 3Dscan vs. Expert Non-expert vs. Expert Cervical height 11 (1%) 7 8 50 Knee height 10 (3%) 6 Mid neck girth 11 (4%) 6 13 19 Neck base girth 11 (3%) 11 17 19 Shoulder length 14 (13%) 4 13 26 Chest girth 21 (3%) 15 19 24 Back Armpits Contour 20 (7%) 10 8 65 Waist girth 18 (3%) 11 20 24 Seat girth 12 (2%) 12 10 57 Arm length 13 (3%) 6 21 32 Outside leg length 13 (2%) 13 11 Thigh girth 14 (4%) 6 36 Knee girth 8 (3%) 4 18 Accuracy (MAD in mm)
- 32. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Kidsize vs. 3Dscan Expert vs. Expert* 3Dscan vs. Expert Non-expert vs. Expert Cervical height 11 (1%) 7 8 50 Knee height 10 (3%) 6 Mid neck girth 11 (4%) 6 13 19 Neck base girth 11 (3%) 11 17 19 Shoulder length 14 (13%) 4 13 26 Chest girth 21 (3%) 15 19 24 Back Armpits Contour 20 (7%) 10 8 65 Waist girth 18 (3%) 11 20 24 Seat girth 12 (2%) 12 10 57 Arm length 13 (3%) 6 21 32 Outside leg length 13 (2%) 13 11 Thigh girth 14 (4%) 6 36 Knee girth 8 (3%) 4 18 Accuracy (MAD in mm) 3D scan kidsize 3D scan kidsize 3D scan kidsize 3D scan kidsize 3D scan kidsize 3D scan kidsize
- 33. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Size advice testing 19 garments Subjective assessment Interview with retailers ‘Think aloud’ with parents Objective assessment Kidsize vs. parents’ choice based on real try-on Locations Bóboli shop IBV Lab Participants 30 children Aged 0-12 y.o. (23 parents)
- 34. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Size advice reliability Labelling (age) Size guide (stature) Kidsize Expert 42% 54% 85% Parents 48% 59% 88%
- 35. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 36. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Conclusions body measuring instrument Size advice & fit prediction algorithms
- 37. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Conclusions body measuring instrument Size advice & fit prediction algorithms
- 38. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Conclusions body measuring instrument Size advice & fit prediction algorithms
- 39. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Introduction Body measuring app Size advice algorithms Validation & results Conclusions Ongoing work
- 40. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Ongoing work & milestones 3D body reconstruction of adults Robustness of segmentation algorithms
- 41. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Ongoing work & milestones 3D body reconstruction of adults Robustness of segmentation algorithms Improvement of 2D3D reconstruction Validation of app with 200 adults Health risk indicators 3D body reconstruction from RGB-D shots Size advice for footwear
- 42. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland • FP7-SME-2013-606091. "Development of a new extended product-service to overcome size assignment and fitting barriers for children fashion on-line market addressing customer needs" (KidSize), FP7, EC • Alemany, et al. (2013). A Exploitation of 3D body databases to improve size selection on the apparel industry, 4th Int Conf on 3D Body Scanning Technologies, Long Beach, CA, USA, November 2013 • Ballester et al. (2014). 3D-based resources fostering the analysis, use, and exploitation of available body anthropometric data. 5th Int Conf on 3D Body Scanning Tech, Hometrica Consulting, Lugano, Switzerland • Barrios et al. (2016). Reliability and criterion validity of self-measured waist, hip, and neck circumferences. BMC Medical Research Methodology, 16(1) • Bradtmiller & Gross (1999). 3D Whole Body Scans: Measurement Extraction Software Validation. • Dekker (2000). 3D human body modelling from range data (Doctoral). University of London. • Gordon et al. (1989). 1988 Anthropometric Survey of US Army Personnel-Methods and Summary Statistics • Han et al. (2010). Comparative analysis of 3D body scan measurements and manual measurements of size Korea adult females. International Journal of Industrial Ergonomics, 40(5), 530-540 • Lu & Wang (2010). The Evaluation of Scan-Derived Anthropometric Measurements. IEEE Transactions on Instrumentation and Measurement, 59(8), 2048-2054 • Parrilla et al. (2015). Low-cost 3D foot scanner using a mobile app. Footwear Science, 7(sup1), S26–S28 • Robinette & Daanen (2006). Precision of the CAESAR scan-extracted measurements. Appl Erg, 37(3), 259-265 • Yoon & Radwin (1994). The accuracy of consumer-made body measurements for women’s mail-order clothing. Human Factors: The Journal of the Human Factors and Ergonomics Society, 36(3), 557–568 References
- 43. 7th Int. Conference on Body Scanning Technologies 1st December 2016, Lugano, Switzerland Alfredo Ballester alfredo.ballester@ibv.upv.es Ana Piérola ana.pierola@ibv.upv.es Sandra Alemany Eduardo Parrilla Jordi Uriel Cristina Pérez Paola Piqueras Beatriz Nácher Clara Solves Julio Vivas Silvia San Jerónimo Juan C. González anthropometry.ibv.org www.kidsizesolution.com Thank you !
Editor's Notes
- Kidsize provides size advice according to the brand (to be worn right now) and to parents (usually allowing for growth) as well as fit predictions. Size recommendation is given as traffic lights with colour coded information. (Screenshots). Different areas considered for different garments, different areas are shown for different garments. Fit predictions are especially important when you have to compromise on fit in order to buy the garment. The two brands who participated in the project were interested in both types of information being given. Unsatisfied expectations.
- Kidsize provides size advice according to the brand (to be worn right now) and to parents (usually allowing for growth) as well as fit predictions. Size recommendation is given as traffic lights with colour coded information. (Screenshots). Different areas considered for different garments, different areas are shown for different garments. Fit predictions are especially important when you have to compromise on fit in order to buy the garment. The two brands who participated in the project were interested in both types of information being given. Unsatisfied expectations.
- Kidsize provides size advice according to the brand (to be worn right now) and to parents (usually allowing for growth) as well as fit predictions. Size recommendation is given as traffic lights with colour coded information. (Screenshots). Different areas considered for different garments, different areas are shown for different garments. Fit predictions are especially important when you have to compromise on fit in order to buy the garment. The two brands who participated in the project were interested in both types of information being given. Unsatisfied expectations.
- Our algorithms were trained using experimental data from over 1100 fit trials. 11 garments of different types where provided in all their size spans by two childrenswear brands (French and Spanish). 160 children aged 0-12 y.o. and their parents voluntarily participated in the fit trials. Children were measured using traditional methods (0-3 y.o.) or using Vitus XXL 3D body scanner (3-12 y.o.). At each fit trial, the size was evaluated by a fashion expert and by parents who declared if they would pick the size or not. Moreover, the expert also assessed the fit at the relevant body areas. These areas were defined were depending on the type of garment.
- We chose to use Ordinal Logistic Regression Models with Stepwise variable selection. A total of 12 different models were defined according to: the source of advice (brand and parents), the age (babies 0-3 y.o. and children 3-12 y.o.) and the type of garment (upper body, lower body and full body). In addition two models (one for babies and one for children) were also defined for each of the 19 body fit areas (38 fit-by-area models in total). The expert rules that applied to the output probabilities of the models were very simple: The recommended size was the biggest size with good fit. If the child was between two sizes (there is no size with a clear good fit), the recommended size was the bigger of the two. If all sizes were big or all sizes were small, the system would not recommend any size. The parents’ advice was always equal or one size bigger than the experts’.
- 19 and 36 body dimensions had been measured respectively for babies and children. The original measurement sets were reduced to 9 variables by the stepwise variable selection (figure 6).
- Expert size != parent size ... Probabilities are consistent Colour coded answers. Child is between sizes Every time the parent clicks on the Kidsize button, the child tries on all the sizes of the garment.
- Only size with all ‘Good’ areas Expert probabilities != parent probs although recommended size is the same
- In order to assess the reliability of the size advices provided by Kidsize Solution, we made a test involving a group of volunteers meeting the target customer profile: parents with one or more children that sometimes buy childrenswear online. 30 children (23 parents) participated in the test. They were selected to have a gender- and age-balanced sample (10 children aged 0-2 years old, 10 children aged 3-7, and 10 children aged 8-12). The childrenswear brands participating in the project (Bóboli and Sucre d’Orge) provided 19 different garments (figure 8) in all the available sizes for the testing. The tests took place at two locations: at a Bóboli shop located in a mall (C.C. Bonaire) in Valencia (figure 9) and at a simulated multi-brand shop at IBV facilities. We prepared a fully working online demo shop including the Kidsize add-on button in the product selection page so that the participants could use it on a tablet during the tests (figure 9). Temporarily, Kidsize app was uploaded to Google Play in order to facilitate its distribution to the testers (figure 9). During the test, each participant was asked to use the Kidsize app to measure his/her child and to go shopping four garments at the online demo shop. The four selected garments were randomly assigned to each participant according to a balanced design of experiments. For each of the garments evaluated, the parents tested first the size (or sizes) advised by Kidsize (expert’s and parents) and were encouraged to test other sizes. Then, the parents were requested to pick one size for wearing the garment straight way and to pick the size that they would buy (usually letting room for the child to grow); they were allowed to select the same size in both cases. At the end of the test, they were also requested to answer some questions about their experience using Kidsize.
- The reliability of the size advice provided by Kidsize was compared with the available alternatives, i.e. a size guide based on children stature provided by the brands, and the labelling of garment sizes, which uses the ages as reference. Results are presented in Table 4. Regarding the subjective evaluation, Kidsize solution was positively assessed by parents, who found it very promising for resolving their concerns when buying without trying on the garments. In general, it was considered very useful, reliable and easy-to-use. Some of the respondents requested a similar tool for adults.