Usability with Project Lecture 10 – 10/10/08

© Simeon Keates 2008

Usability with ProjectLecture 10 – 10/10/08Dr. Simeon Keates


Exercise – part 1

Last week you were asked to bring in 4 items• Landline telephone• Mobile telephone• TV remote control• 1 other item

This week…

Perform exclusion calculations on each product using the data on:• http://www.eng.cam.ac.uk/inclusivedesign/


Exercise – part 2

Identify the common methods of interacting with the product

Identify which of the 7 DFS capability scales are involved in the interaction

Based on the DFS scales, estimate the limiting capability demand for each scale


Exercise – part 3

Report the number and %age of people excluded by each capability demand• For 16+ and 75+

Report the total number and %age of people excluded by the product• For 16+ and 75+

Prepare a 5 minute presentation to discuss:• Your exclusion calculation assumptions• Your exclusion calculation results• What were the principal causes of exclusion?• What do you think should be done to reduce the exclusion for each product?


What is “reasonable accommodation”?


Defining “reasonable accommodation”

Must offer “reasonable accommodation”• BUT what is reasonable?

Not defined explicitly• Companies left guessing

Will be defined in courts• Major risk/headache for companies


Attitudes to “reasonable accommodation”

EQUITABLE ACCESS

MINIMUM(compliance)

Access to functionality

IDEALAccess to

functionalityin same time

EQUITABLE ACCESS

MINIMUM(compliance)

Access to functionality

IDEALAccess to

functionalityin same timeIDEOLOGICAL

DIVIDE

Prag

mat

ists

Idea

lists


Interesting questions for companies

Is the equitable access ideal possible?• Is the equitable access minimum possible?• “ Equal, but different ” problem

Users with functional impairments => longer times

Can technology always make up the difference in user capabilities?

3 case studies…


Case study 1: The personal information point


The information point accessibility assessment

Sensory assessment: Screen too high and not adjustable Audio output not duplicated Visual output not duplicated

Motor assessment: Need to stand Reaching and dexterity demands

45% of target users excluded

Is this “reasonable”?


Case study 2 – Cursor assistance for motor-impaired users

Symptoms that can affect cursor control:

Tremor Spasm Restricted motion Reduced strength Poor co-ordination


User group behaviours

0

0.5

1

1.5

2

2.5

3

3.5

Group OA Group P Group Y Group A

0

20

40

60

80

100

120


Target activation times

Peak velocities

No. of incorrect clicks

0

1

2

3

4

5



Summarising the differences

Younger adults (IBM interns)• Shortest (1), fastest (1), more errors (3) - slapdash

• “I can fix it”• Games culture?

Adults (IBM regulars)• Shorter (2), faster (2), fewest errors (1)

• Best compromise between speed and accuracy? Parkinson’s users• Longer (3), slowest (4), fewer errors (2)

• Slow, but sure Older adults• Longest (4), slower (3), most errors (4)

• Vision difficulties?• Lack of experience


A method of cursor assistance

Haptic gravity wells:

Target

Gravity well

Attractive force


Experimental set-up


The effect of gravity wells

Target


Results - Throughput

0

2

4

6

8

10

MI AB


Case study 2 summary

Haptic gravity wells are clearly very helpful MI users “with” on similar level to AB users “without”

BUT: AB users also improve “with” Is this “equal” time? Is this “reasonable”???


Case study 3 – Paperless office

AN Other wants to move to a paperless office• Currently receives 3.5 million pages per day

Paper documents are stored as TIFFs

Section 508 accessibility requirements• Sight-impaired• Low vision

Current solution – employ readers• “ Equal, but different. ”• Is this reasonable?


The study documents

Almost fully unconstrained Content:• Unconstrained vocabulary

Text:• Typed• Handwritten• Annotated• Stamps

Graphical content:• Diagrams• Charts • Graphs


Examples of the study documents


Examples of the study documents (cont.)


Readability metrics (text)

Translation rates:• Character-by-character• Word-by-word

Number and %ages of errors:• Level 1 - Minor• Level 2 - Moderate• Level 3 - Serious


TIFF file

OCR – The scanning process..................................11111.........11...11.......11.....11......11.....11.............11..........11111........111..11.......11....11......11.....11......11.....11......11....111......111..1.11.1.....1111..111..................

111

1

1

1

11 11

11

1

11

1111

11

1

111 1111

11 1

11

1

1 111

11

111 1

11 1

1

111111 1

1 1

1

1


OCR – Possible sources of scanning errors

Data LOSS NOISE


Comparing three OCR engines

“…also develop the skills to invert containers to get objects *inside. He should begin to find small details in a favorite picture book (a bird in a *tree, a small fish in the *ocean). His understanding of familiar objects should…”

FineReader:

“…*also *develop *the *skills *to *irxvert *containers *to *get *ob^ects *inside. *?e *should *begin *to *Znd *small *details *i? *a favorite *picture *baa? *?a *bird *in *a *true, *a *small *ash *in *the *ocean}. *his *understanding *of *familiar *ob^ects *should…”

OmniPage:

“…*also *de???op *the *s?il?s *ta *ivart?an#ainer?to *e?ob??cts?n?id?. *?e *shau?ti *b?ta *Znd *srnali *details *i?a *favarita *picture *baa??bi?rd *in *a *tra?,a *srr?a????in *tk?e *o?ean}. *?is *und?rt?a?af *fa.?i?iar *ob?ects *hau?d *co??i?u?ta *de?eiap *d?i?houi d…”

Recognita:


OCR results – Calculating the error rates

Record the document properties• # of words, characters• Font types (e.g. typed, handwritten) and sizes

Count instances of error types• Redaction errors• Spaces +ed, -ed• Format errors (e.g. wrong case, incorrect text positioning)• Extraction errors (i.e. incorrect translation)

• By character• By word

Classify severity• Level 1 – minor• Level 2 – moderate• Level 3 – severe

Calculate %age error rates

Note: classification for sighted users


OCR results – An example extracted document – 1

Extracted text:

*evaluators, shQWfag’an interest in imitating words *and sp *eech.^j^kd real words along^vith j argon to exjgpss. himself . *dflffVily indicated that they understand most of what tie *says.^H^^owedhisuse of two+ word phrases

Original text:[Typed page document]



Extracted text:*IBISES6?? *fc?day *?P *a *yearly *SJn *exam’ *She *is *a *40 *êar *old *white *feraale status post âginal hysterectomy five years ago. She has continued to have some difficulty with loss || of urine upon coughing or sneezing. I had given her some samples of Ditropan last year but || *SShZ *^ *t0 *^ *theSe’ *ShS *feelS *that *her *wei^ contributes a ^reatleal *Z *££ problems *with *mcontmence She has had some continuing problems with depressive *sympW *Sê cries very easily and it is getting a little bit worse. She also feels very *withdrawn *She tells roe that her sister in Florida had a similar history and was on *Paxil and did.

Original text:[Typed page with notes document]



Extracted text:

*2j*»rlfar Cardiology || *^^m Chart: *3£4U3& *Dr *-^ || *0 _. *, Medications: *Adenosinc *Dose: || Dose: *jjj&f»- *f-^- *\ *Dobutaimne

Original text:[Pictures and Graphs document]


OCR results – Overall word error % rates

Typed page 6.50 %(1 word in 15)

Typed page with notes

8.12 %(1 word in 12)

Faxes 14.45 %(1 word in 7)

Pictures and graphs

23.45 %(1 word in 4)

Handwritten reports

36.35 %(1 word in 3)

EKGs 49.72 %(1 word in 2)

A “typical sentence” contains 7 words.

An extraction error rate of 6.5% equates to 1 word error every 2 sentences.


OCR results – Context metrics Text location awareness – PARTLY SATISFIED – columns only

• Does the data extraction technology output provide an indication of where the text is on the page? Table search – VERY LIMITED – recognised individual columns, not tables

• Does the data extraction technology recognise tables and support searching within them? Diagram detection – VERY LIMITED – recognised as “not text”

• Does the data extraction technology recognise diagrams and support searching within them? Graph detection – VERY LIMITED – as for diagram detection

• Does the data extraction technology recognise graphs (charts) and support searching within them? Dealing with uncertainty – SATISFIED – all engines highlighted uncertain text

• Does the data extraction technology recognise entities on the page that it cannot translate and highlight this?

Text emphasis – PARTLY SATISFIED – could, but not always correct• Does the data extraction technology recognise when the author of the document has selected a

particular item of text for special emphasis? Multiple selection lists – VERY LIMITED – words and columns, but no “meta” info

• Does the data extraction technology recognise multiple selection lists and can it identify the item(s) selected?


Conclusions of OCR investigation

“ Current OCR technology is not capable of providing an acceptable level of text extraction from medical evidence as it is now received. ”

“ Technology cannot provide equitable access in this case. Alternative methods are required. ”

“ Equal, but different. ”


Overall summary

Some products clearly not “reasonable”• Case study 1

Technology cannot always make up for lack of user capability• Case study 2• Even when it does – the goalposts move!!!


Conclusion

What is needed is a framework for evaluating “reasonableness”

Based on quantifiable metrics

Reliable, repeatable, consistent, robust


A framework for assessing acceptability – 1

Stage 1 – Identify each target user group/persona• e.g. blind users, >65s, etc.

Stage 2 – Identify each component step in the interaction per group• e.g. press Enter, activate OK button, move cursor to icon, etc.

Stage 3 – Compare number of steps per group• e.g. 10 for able-bodied, 30 for blind using screen reader

DECISION GATEWAY 1 Are the numbers of steps roughly equal?

If not – differences need to be justified or remedied



Stage 4 – Perform user studies with baseline user group• Calculate times, error rates, etc.

Stage 5 – Perform user studies with target user groups• Calculate times, error rates, etc.

DECISION GATEWAY 2 Could all of the users complete the task?

If not – causes of difficulties need to be removed or remedied



Stage 6 – Compare error rates for each group• e.g. 2 per trial able-bodied, 5 per trial blind using screen-reader

DECISION GATEWAY 3 Are the error rates the same or similar across user groups?

If not – significant differences have to be justified or remedied



Stage 7 – Compare times to complete tasks for each group + modifiers• e.g. number of component steps per group +• proportion of component steps affected by group disabilities +• relative importance of each step (3 = critical, 1 = peripheral) +• relative severity of the level of disability +• additional latencies from AT used

DECISION GATEWAY 4 Are the modified times the same or similar across user groups?

If not – significant differences have to be justified or remedied


When we come back…

User trials• How to plan the trials• How to select users• How to conduct the sessions• How to analyse the data gathered• How to make design recommendations

Designing and evaluating for unusual circumstances• Airports• Mobile phones

Making the business case for usability• How to calculate the “bottom line” impact

Project• Finishing your design and then testing with “real” people!


Exercise


Exercise – part 1

Perform an exclusion analysis on your web-site • (As you did on Wednesday)

Prepare a summary of your calculation• Assumptions• Levels of capability required• Exclusion (total and %age) for 16+ and 75+

Make any changes necessary to your site• + any outstanding ones from last couple of weeks


And finally…

Turn to the back page of today’s handout…

Usability with Project Lecture 10 – 10/10/08

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Transcript of Usability with Project Lecture 10 – 10/10/08