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Designed To Fit

Challenges of Interaction Design for
Clothes Fitting Room Technologies.
This paper uncovers issues in the design of camera-based technologies
to support retail shopping in a physical store, specifically clothes shopping.
An emerging class of technology is targeting the enhancement of retail shopping,
including the trying on of clothing. Designing such systems requires careful
considerations of physical and electronic design, as well as concerns about
user privacy. We explore the entire design cycle using a technology concept
called the Responsive Mirror through its conception, prototyping and evaluation.
The Responsive Mirror is an implicitly controlled video technology for
clothes fitting rooms that allows a shopper to directly compare a currently worn
garment with images from the previously worn garment. The orientation of images
from past trials is matched to the shopper’s pose as he moves. To explore
the tension between privacy and publicity, the system also allows comparison to
clothes that other people in the shoppers’ social network are wearing. A user
study elicited a number of design tradeoffs regarding privacy, adoption, benefits
to shoppers and merchants and user behaviors in fitting rooms.

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Designed To Fit

  1. 1. Designed to Fit Challenges of Interaction Design for Clothes Fitting Room Technologies Bo Begole , parc Takashi Matsumoto , Keio University Wei Zhang , Oregon State University Nicholas Yee , parc Juan Liu , parc Maurice Chu , parc HCI International, July 2009
  2. 2. Implicit Interaction <ul><li>Conventional systems: User initiates interaction and drives the system </li></ul><ul><li>Implicit Interaction : System perceives state of user and situation and acts on users’ inferred goals </li></ul><ul><ul><li>Simplifies user experience </li></ul></ul><ul><ul><li>Little or no training required </li></ul></ul>Responsive Mirror Responsive Technologies Information Recommendation Clothing Recognition Psychographic Profiling [ ICDSC 2008 ] [ IUI 2008, HCII 2009 ] [ CHI 2008 ] Magitti Related PARC Research: also: Human-Robot Interaction Multi-party conversations Camera-based tracking MIT Media Lab Xbox Natal
  3. 3. Clothes Shopping as an Information Seeking Activity Browse Filter Evaluate Decide, Buy Availability, cost, size, colors, texture, feel, fit, style trends, etc. In-Store Online
  4. 4. Related Work Apparel Fitting Technologies <ul><li>Virtual fitting technologies </li></ul><ul><ul><li>Project image of clothing on image of shopper or 3D model </li></ul></ul><ul><ul><li>Helps pre-filter but ultimately clothing must be tried on </li></ul></ul><ul><li>Fitting room technologies </li></ul><ul><ul><li>Detect clothing items and retrieve information – price, colors, in stock </li></ul></ul><ul><ul><li>Record videos of fitting </li></ul></ul><ul><ul><li>Send to friends for comments </li></ul></ul><ul><ul><li>Project virtual clothing on mirror </li></ul></ul><ul><ul><li>Don’t provide access to prior trials </li></ul></ul><ul><ul><li>Don’t show social context of fashion </li></ul></ul>
  5. 5. Responsive Mirror Implicitly controlled vision-based system providing information for “self” comparison and “social” context The Future of … Dressing Rooms , BNet, Sumi Das video (min1:25-2:02)
  6. 6. Privacy Considerations <ul><li>What are the implications of introducing a camera to this semi-private setting? </li></ul><ul><li>Altman’s three boundaries </li></ul><ul><ul><li>Disclosure – clothes fittings are typically only shared with co-present shoppers, friends, family </li></ul></ul><ul><ul><li>Identity – Apparel trials are a time when a person is experimenting with their “presentation of self” </li></ul></ul><ul><ul><li>Temporal – fitting sessions are usually ephemeral, not preserved for future scrutiny </li></ul></ul><ul><li>Social aspects </li></ul><ul><ul><li>What image do I project when wearing these clothes? </li></ul></ul><ul><ul><li>What do other people who wear these clothes look like? </li></ul></ul>
  7. 7. Formative Study <ul><li>Quick examination of privacy concerns </li></ul><ul><li>Participants </li></ul><ul><ul><li>12 males, 28-52 years </li></ul></ul><ul><ul><ul><li>Limited population </li></ul></ul></ul><ul><ul><ul><li>Clothes matching algorithm only worked on men’s shirts </li></ul></ul></ul><ul><li>Within subjects analysis </li></ul><ul><li>Three Conditions </li></ul><ul><ul><li>Mirror alone </li></ul></ul><ul><ul><li>Mirror with Previous Outfit </li></ul></ul><ul><ul><li>Mirror with Other people in similar outfits </li></ul></ul><ul><li>Task </li></ul><ul><ul><li>Select one out of 6 shirts for each condition (18 shirts total) </li></ul></ul>
  8. 8. Results - Overall <ul><li>Buying decisions were not different across conditions </li></ul><ul><ul><li>The characteristics of the clothing mattered more than the technology </li></ul></ul><ul><li>Which condition did you prefer? (1=highest, 5=lowest) </li></ul><ul><ul><li>Previous Outfit plus Other People (M = 1.92) (not experienced) </li></ul></ul><ul><ul><li>Previous Outfit (M = 2.00) </li></ul></ul><ul><ul><li>Other People (M = 2.83) </li></ul></ul><ul><ul><li>Plain Mirror (M = 3.25) (  2 = 9.10, p = .03) </li></ul></ul><ul><li>Which condition was more helpful? </li></ul><ul><ul><li>Previous Outfit (M = 3.00) </li></ul></ul><ul><ul><li>Other People (M = 2.5) </li></ul></ul>
  9. 9. Results - Disclosure <ul><li>How bothered by people in the following groups seeing images from the fittings? </li></ul><ul><ul><li>(5=bothers me a great deal, 1=doesn’t bother me at all) </li></ul></ul><ul><ul><li>Family (M=1.08) </li></ul></ul><ul><ul><li>Friends (M=1.50) </li></ul></ul><ul><ul><li>Stranger (M=2.08) </li></ul></ul><ul><ul><li>Co-worker (M=2.25) </li></ul></ul><ul><ul><li>No significant effect regarding gender </li></ul></ul><ul><ul><li>Level of concern was significantly higher for bad shirts (M=3.0) versus good shirts (M=1.42) (p = .001) </li></ul></ul><ul><li>Implication: Access control at just two levels: </li></ul><ul><ul><li>Friends and Family </li></ul></ul><ul><ul><li>Co-workers and Strangers </li></ul></ul>not sig. diff’t not sig. diff’t sig. diff’t
  10. 10. Results - Identity <ul><li>How often do you think about </li></ul><ul><ul><li>(5=Always, Often, Sometimes, Seldom, Never=1) </li></ul></ul><ul><ul><li>Someone you know who might like these clothes (M=2.67, SD=0.98) </li></ul></ul><ul><ul><li>How similar to what other people you know are wearing (M=2.92, SD=0.9) </li></ul></ul><ul><ul><li>How similar to what other people you don’t know are wearing (M=2.33, SD=0.98) </li></ul></ul><ul><li>Implication: Providing information on what other people are wearing would be useful sometimes </li></ul>
  11. 11. Results – Temporality <ul><li>Would you want to remove images in the future if your tastes change? (5=Definitely, 1=Definitely Not) </li></ul><ul><ul><li>(M=3.08, SD=1.16) (closest to Possibly) </li></ul></ul><ul><li>Preferred periods of time </li></ul><ul><ul><li>within 3 months (5 participants) </li></ul></ul><ul><ul><li>within 1 year (5 participants) </li></ul></ul><ul><ul><li>never (2 participants) </li></ul></ul><ul><li>Implication: systems can remind users at 3 month and 1 year period to review their image record </li></ul>
  12. 12. Summary & Conclusions <ul><li>Physical apparel shopping requires different information than online shopping </li></ul><ul><ul><li>Apparel fit, feel, drape, texture, translucency, etc. </li></ul></ul><ul><li>Privacy is a concern but not a block </li></ul><ul><ul><li>understanding user concerns in Altman’s three boundaries can help system designers </li></ul></ul><ul><li>Future Directions </li></ul><ul><ul><li>Countertop version for eyeglasses, jewelry, hats, makeup, hair, … </li></ul></ul><ul><ul><li>Front-camera tracking algorithms </li></ul></ul><ul><ul><li>Adding sales support to the system </li></ul></ul><ul><li>Contacts welcome! </li></ul><ul><ul><li>[email_address] </li></ul></ul>Counter-top Responsive Mirror
  13. 13. The end
  14. 14. In-Store vs. Online Goal: Bring the best of both worlds together Pros Cons In-Store Browse/ Select Touch and feel clothing Limited to in-store inventory Evaluate Can evaluate tactile fit and visual style, Real-time feedback Comparisons conducted sequentially, Feedback limited to co-present friends Online Browse/ Select Huge selection of clothing Only visual look of clothing Evaluate Can compare multiple clothing simultaneously, Feedback from large number of people Can only evaluate visual style, No real-time feedback from friends