Television, computer and portable display device use by people with central vision impairment

Woods, Russell L.; Satgunam, PremNandhini

doi:10.1111/j.1475-1313.2011.00833.x

Cited by 28 publications

(46 citation statements)

References 31 publications

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“…Since amount of TV watched, difficulties watching TV, frequency of watching movies, and likelihood of watching video on a handheld device have been found to be related to age [26], we conducted post hoc logistic and linear regressions that included age as a predictor. The difference in age distribution fully explained the difference in amount of handheld video watching and frequency of moviegoing.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection

Saunders¹,

Bex²,

Woods³

2013

J Med Internet Res

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BackgroundCrowdsourcing has become a valuable method for collecting medical research data. This approach, recruiting through open calls on the Web, is particularly useful for assembling large normative datasets. However, it is not known how natural language datasets collected over the Web differ from those collected under controlled laboratory conditions.ObjectiveTo compare the natural language responses obtained from a crowdsourced sample of participants with responses collected in a conventional laboratory setting from participants recruited according to specific age and gender criteria.MethodsWe collected natural language descriptions of 200 half-minute movie clips, from Amazon Mechanical Turk workers (crowdsourced) and 60 participants recruited from the community (lab-sourced). Crowdsourced participants responded to as many clips as they wanted and typed their responses, whereas lab-sourced participants gave spoken responses to 40 clips, and their responses were transcribed. The content of the responses was evaluated using a take-one-out procedure, which compared responses to other responses to the same clip and to other clips, with a comparison of the average number of shared words.ResultsIn contrast to the 13 months of recruiting that was required to collect normative data from 60 lab-sourced participants (with specific demographic characteristics), only 34 days were needed to collect normative data from 99 crowdsourced participants (contributing a median of 22 responses). The majority of crowdsourced workers were female, and the median age was 35 years, lower than the lab-sourced median of 62 years but similar to the median age of the US population. The responses contributed by the crowdsourced participants were longer on average, that is, 33 words compared to 28 words (P<.001), and they used a less varied vocabulary. However, there was strong similarity in the words used to describe a particular clip between the two datasets, as a cross-dataset count of shared words showed (P<.001). Within both datasets, responses contained substantial relevant content, with more words in common with responses to the same clip than to other clips (P<.001). There was evidence that responses from female and older crowdsourced participants had more shared words (P=.004 and .01 respectively), whereas younger participants had higher numbers of shared words in the lab-sourced population (P=.01).ConclusionsCrowdsourcing is an effective approach to quickly and economically collect a large reliable dataset of normative natural language responses.

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Section: Discussionmentioning

confidence: 99%

Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection

Saunders¹,

Bex²,

Woods³

2013

J Med Internet Res

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“…[2][3][4][5] Such techniques can be applied to activities of daily living such as TV watching and internet use. A recent survey study 6 has determined that visually impaired people have similar video viewing habits as normally sighted people, and most visually impaired people are interested in image enhancement technologies for TV watching and computer use. 4,6 Prior studies have found that visually impaired prefer image enhancement in both static images 5,7,8 and videos.…”

Section: Introductionmentioning

confidence: 99%

Visual search performance of patients with vision impairment: effect of JPEG image enhancement

Luo

Satgunam

Peli

2012

Ophthalmic Physiologic Optic

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Purpose To measure natural image search performance in patients with central vision impairment. To evaluate the performance effect for a JPEG based image enhancement technique using the visual search task. Method 150 JPEG images were presented on a touch screen monitor in either an enhanced or original version to 19 patients (visual acuity 0.4 to 1.2 logMAR, 6/15 to 6/90, 20/50 to 20/300) and 7 normally sighted controls (visual acuity −0.12 to 0.1 logMAR, 6/4.5 to 6/7.5, 20/15 to 20/25). Each image fell into one of three categories: faces, indoors, and collections. The enhancement was realized by moderately boosting a mid-range spatial frequency band in the discrete cosine transform (DCT) coefficients of the image luminance component. Participants pointed to an object in a picture that matched a given target displayed at the upper-left corner of the monitor. Search performance was quantified by the percentage of correct responses, the median search time of correct responses, and an “integrated performance” measure – the area under the curve of cumulative correct response rate over search time. Results Patients were able to perform the search tasks but their performance was substantially worse than the controls. Search performances for the 3 image categories were significantly different (p≤0.001) for all the participants, with searching for faces being the most difficult. When search time and correct response were analyzed separately, the effect of enhancement led to increase in one measure but decrease in another for many patients. Using the integrated performance, it was found that search performance declined with decrease in acuity (p=0.005). An improvement with enhancement was found mainly for the patients whose acuity ranged from 0.4 to 0.8 logMAR (6/15 to 6/38, 20/50 to 20/125). Enhancement conferred a small but significant improvement in integrated performance for indoor and collection images (p=0.025) in the patients. Conclusion Search performance for natural images can be measured in patients with impaired vision to evaluate the effect of image enhancement. Patients with moderate vision loss might benefit from the moderate level of enhancement used here.

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“…1 Electronic image enhancement may be a viable vision rehabilitation approach. The increasing trend for electronic dissemination of information makes it important to develop and evaluate image enhancement techniques that can serve the needs of a growing population with vision impairments.…”

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Effects of Contour Enhancement on Low-Vision Preference and Visual Search

Satgunam¹,

Woods

Luo³

et al. 2012

Optometry and Vision Science

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PURPOSE To determine whether image enhancement improves visual search performance and whether enhanced images were also preferred by subjects with vision impairment. METHOD Subjects (n=24) with vision impairment (vision: 20/52 to 20/240) completed visual search and preference tasks for 150 static images that were enhanced to increase object contours’ visual saliency. Subjects were divided into two groups and were shown three enhancement levels. Original and Medium enhancement were shown to both groups. High enhancement was shown to Group 1 and Low was shown to Group 2. For search, subjects pointed to an object that matched a search target displayed at the top left of the screen. An “integrated search performance” measure (area under the curve of cumulative correct response rate over search time) quantified performance. For preference, subjects indicated the preferred side when viewing the same image with different enhancement levels on side-by-side HDTVs. RESULTS Contour enhancement did not improve performance in the visual search task. Group 1 subjects significantly (p<0.001) rejected the High enhancement, and showed no preference for Medium enhancement over the Original images. Group 2 subjects significantly preferred (p<0.001) both the Medium and the Low enhancement levels over Original. Contrast sensitivity was correlated with both preference and performance; subjects with worse contrast sensitivity performed worse in the search task (ρ=0.77, p<0.001) and preferred more enhancement (ρ=−0.47, p=0.02). No correlation between visual search performance and enhancement preference was found. However, a small group of subjects (n=6) in a narrow range of mid-contrast sensitivity performed better with the enhancement and most (n=5) also preferred the enhancement. CONCLUSIONS Preferences for image enhancement can be dissociated from search performance in people with vision impairment. Further investigations are needed to study the relationships between preference and performance for a narrow range of mid-contrast sensitivity where a beneficial effect of enhancement may exist.

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Television, computer and portable display device use by people with central vision impairment

Cited by 28 publications

References 31 publications

Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection

Crowdsourcing a Normative Natural Language Dataset: A Comparison of Amazon Mechanical Turk and In-Lab Data Collection

Visual search performance of patients with vision impairment: effect of JPEG image enhancement

Effects of Contour Enhancement on Low-Vision Preference and Visual Search

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