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

Implicit Interaction Conventional systems: User initiates interaction and drives the system Implicit Interaction : System perceives state of user and situation and acts on users’ inferred goals Simplifies user experience Little or no training required 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

Conventional systems: User initiates interaction and drives the system

Implicit Interaction : System perceives state of user and situation and acts on users’ inferred goals

Simplifies user experience

Little or no training required

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

Related Work Apparel Fitting Technologies Virtual fitting technologies Project image of clothing on image of shopper or 3D model Helps pre-filter but ultimately clothing must be tried on Fitting room technologies Detect clothing items and retrieve information – price, colors, in stock Record videos of fitting Send to friends for comments Project virtual clothing on mirror Don’t provide access to prior trials Don’t show social context of fashion

Virtual fitting technologies

Project image of clothing on image of shopper or 3D model

Helps pre-filter but ultimately clothing must be tried on

Fitting room technologies

Detect clothing items and retrieve information – price, colors, in stock

Record videos of fitting

Send to friends for comments

Project virtual clothing on mirror

Don’t provide access to prior trials

Don’t show social context of fashion

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)

Privacy Considerations What are the implications of introducing a camera to this semi-private setting? Altman’s three boundaries Disclosure – clothes fittings are typically only shared with co-present shoppers, friends, family Identity – Apparel trials are a time when a person is experimenting with their “presentation of self” Temporal – fitting sessions are usually ephemeral, not preserved for future scrutiny Social aspects What image do I project when wearing these clothes? What do other people who wear these clothes look like?

What are the implications of introducing a camera to this semi-private setting?

Altman’s three boundaries

Disclosure – clothes fittings are typically only shared with co-present shoppers, friends, family

Identity – Apparel trials are a time when a person is experimenting with their “presentation of self”

Temporal – fitting sessions are usually ephemeral, not preserved for future scrutiny

Social aspects

What image do I project when wearing these clothes?

What do other people who wear these clothes look like?

Formative Study Quick examination of privacy concerns Participants 12 males, 28-52 years Limited population Clothes matching algorithm only worked on men’s shirts Within subjects analysis Three Conditions Mirror alone Mirror with Previous Outfit Mirror with Other people in similar outfits Task Select one out of 6 shirts for each condition (18 shirts total)

Quick examination of privacy concerns

Participants

12 males, 28-52 years

Limited population

Clothes matching algorithm only worked on men’s shirts

Within subjects analysis

Three Conditions

Mirror alone

Mirror with Previous Outfit

Mirror with Other people in similar outfits

Task

Select one out of 6 shirts for each condition (18 shirts total)

Results - Overall Buying decisions were not different across conditions The characteristics of the clothing mattered more than the technology Which condition did you prefer? (1=highest, 5=lowest) Previous Outfit plus Other People (M = 1.92) (not experienced) Previous Outfit (M = 2.00) Other People (M = 2.83) Plain Mirror (M = 3.25) (  2 = 9.10, p = .03) Which condition was more helpful? Previous Outfit (M = 3.00) Other People (M = 2.5)

Buying decisions were not different across conditions

The characteristics of the clothing mattered more than the technology

Which condition did you prefer? (1=highest, 5=lowest)

Previous Outfit plus Other People (M = 1.92) (not experienced)

Previous Outfit (M = 2.00)

Other People (M = 2.83)

Plain Mirror (M = 3.25) (  2 = 9.10, p = .03)

Which condition was more helpful?

Previous Outfit (M = 3.00)

Other People (M = 2.5)

Results - Disclosure How bothered by people in the following groups seeing images from the fittings? (5=bothers me a great deal, 1=doesn’t bother me at all) Family (M=1.08) Friends (M=1.50) Stranger (M=2.08) Co-worker (M=2.25) No significant effect regarding gender Level of concern was significantly higher for bad shirts (M=3.0) versus good shirts (M=1.42) (p = .001) Implication: Access control at just two levels: Friends and Family Co-workers and Strangers not sig. diff’t not sig. diff’t sig. diff’t

How bothered by people in the following groups seeing images from the fittings?

(5=bothers me a great deal, 1=doesn’t bother me at all)

Family (M=1.08)

Friends (M=1.50)

Stranger (M=2.08)

Co-worker (M=2.25)

No significant effect regarding gender

Level of concern was significantly higher for bad shirts (M=3.0) versus good shirts (M=1.42) (p = .001)

Implication: Access control at just two levels:

Friends and Family

Co-workers and Strangers

Results - Identity How often do you think about (5=Always, Often, Sometimes, Seldom, Never=1) Someone you know who might like these clothes (M=2.67, SD=0.98) How similar to what other people you know are wearing (M=2.92, SD=0.9) How similar to what other people you don’t know are wearing (M=2.33, SD=0.98) Implication: Providing information on what other people are wearing would be useful sometimes

How often do you think about

(5=Always, Often, Sometimes, Seldom, Never=1)

Someone you know who might like these clothes (M=2.67, SD=0.98)

How similar to what other people you know are wearing (M=2.92, SD=0.9)

How similar to what other people you don’t know are wearing (M=2.33, SD=0.98)

Implication: Providing information on what other people are wearing would be useful sometimes

Results – Temporality Would you want to remove images in the future if your tastes change? (5=Definitely, 1=Definitely Not) (M=3.08, SD=1.16) (closest to Possibly) Preferred periods of time within 3 months (5 participants) within 1 year (5 participants) never (2 participants) Implication: systems can remind users at 3 month and 1 year period to review their image record

Would you want to remove images in the future if your tastes change? (5=Definitely, 1=Definitely Not)

(M=3.08, SD=1.16) (closest to Possibly)

Preferred periods of time

within 3 months (5 participants)

within 1 year (5 participants)

never (2 participants)

Implication: systems can remind users at 3 month and 1 year period to review their image record

Summary & Conclusions Physical apparel shopping requires different information than online shopping Apparel fit, feel, drape, texture, translucency, etc. Privacy is a concern but not a block understanding user concerns in Altman’s three boundaries can help system designers Future Directions Countertop version for eyeglasses, jewelry, hats, makeup, hair, … Front-camera tracking algorithms Adding sales support to the system Contacts welcome! [email_address] Counter-top Responsive Mirror

Physical apparel shopping requires different information than online shopping

Apparel fit, feel, drape, texture, translucency, etc.

Privacy is a concern but not a block

understanding user concerns in Altman’s three boundaries can help system designers

Future Directions

Countertop version for eyeglasses, jewelry, hats, makeup, hair, …

Front-camera tracking algorithms

Adding sales support to the system

Contacts welcome!

[email_address]

The end

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