Simeon Keates 2008 Usability with Project Lecture 10 10/10/08 Dr. Simeon Keates Simeon Keates 2008 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:Page 2 Simeon Keates 2008 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 Page 3 Simeon Keates 2008 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? Page 4 Simeon Keates 2008 What is reasonable accommodation? Page 5 Simeon Keates 2008 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 Page 6 Simeon Keates 2008 Attitudes to reasonable accommodation EQUITABLE ACCESS MINIMUM (compliance) Access to functionality IDEAL Access to functionality in same time EQUITABLE ACCESS MINIMUM (compliance) Access to functionality IDEAL Access to functionality in same time IDEOLOGICAL DIVIDE Pragmatists Idealists Page 7 Simeon Keates 2008 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 Page 8 Simeon Keates 2008 Case study 1: The personal information point Page 9 Simeon Keates 2008 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? Page 10 Simeon Keates 2008 Case study 2 Cursor assistance for motor-impaired users Symptoms that can affect cursor control: Tremor Spasm Restricted motion Reduced strength Poor co-ordination Page 11 Simeon Keates 2008 User group behaviours Target activation times Peak velocities No. of incorrect clicks Page 12 Simeon Keates 2008 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? Parkinsons 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 Page 13 Simeon Keates 2008 A method of cursor assistance Haptic gravity wells: Target Gravity well Attractive force Page 14 Simeon Keates 2008 Experimental set-up Page 15 Simeon Keates 2008 The effect of gravity wells Target Page 16 Simeon Keates 2008 Results - Throughput Page 17 Simeon Keates 2008 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??? Page 18 Simeon Keates 2008 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? Page 19 Simeon Keates 2008 The study documents Almost fully unconstrained Content: Unconstrained vocabulary Text: Typed Handwritten Annotated Stamps Graphical content: Diagrams Charts Graphs Page 20 Simeon Keates 2008 Examples of the study documents Page 21 Simeon Keates 2008 Examples of the study documents (cont.) Page 22 Simeon Keates 2008 Examples of the study documents (cont.) Page 23 Simeon Keates 2008 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 Page 24 Simeon Keates 2008 TIFF file OCR The scanning process Page 25 Simeon Keates 2008 OCR Possible sources of scanning errors Data LOSSNOISE Page 26 Simeon Keates 2008 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: Page 27 Simeon Keates 2008 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 Page 28 Simeon Keates 2008 OCR results An example extracted document 1 Extracted text: *evaluators, shQWfagan 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] Page 29 Simeon Keates 2008 OCR results An example extracted document 2 Extracted text: *IBISES6?? *fc?day *?P *a *yearly *SJn *exam *She *is *a *40 *^ear *old *white *feraale status post ^aginal 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 *S^e 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] Page 30 Simeon Keates 2008 OCR results An example extracted document 3 Extracted text: *2j*rlfar Cardiology || *^^m Chart: *34U3& *Dr *-^ || *0 _. *, Medications: *Adenosinc *Dose: || Dose: *jjj&f- *f-^- *\ *Dobutaimne Original text: [Pictures and Graphs document] Page 31 Simeon Keates 2008 OCR results Overall word error % rates Typed page6.50 % (1 word in 15) Typed page with notes 8.12 % (1 word in 12) Faxes14.45 % (1 word in 7) Pictures and graphs % (1 word in 4) Handwritten reports % (1 word in 3) EKGs49.72 % (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. Page 32 Simeon Keates 2008 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? Page 33 Simeon Keates 2008 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. Page 34 Simeon Keates 2008 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!!! Page 35 Simeon Keates 2008 Conclusion What is needed is a framework for evaluating reasonableness Based on quantifiable metrics Reliable, repeatable, consistent, robust Page 36 Simeon Keates 2008 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 Page 37 Simeon Keates 2008 A framework for assessing acceptability 2 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 Page 38 Simeon Keates 2008 A framework for assessing acceptability 3 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 Page 39 Simeon Keates 2008 A framework for assessing acceptability 4 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 Page 40 Simeon Keates 2008 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! Page 41 Simeon Keates 2008 Exercise Page 42 Simeon Keates 2008 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 Page 43 Simeon Keates 2008 And finally Turn to the back page of todays handout Page 44