Reading on LCD vs e-Ink displays: Effects on fatigue and visual strain
Institute for Research in Open-, Distance- and eLearning, Swiss Distance University of Applied Sciences, Brig, Switzerland. Ophthalmic and Physiological Optics
(Impact Factor: 2.18).
07/2012; 32(5):367-74. DOI: 10.1111/j.1475-1313.2012.00928.x
Most recently light and mobile reading devices with high display resolutions have become popular and they may open new possibilities for reading applications in education, business and the private sector. The ability to adapt font size may also open new reading opportunities for people with impaired or low vision. Based on their display technology two major groups of reading devices can be distinguished. One type, predominantly found in dedicated e-book readers, uses electronic paper also known as e-Ink. Other devices, mostly multifunction tablet-PCs, are equipped with backlit LCD displays. While it has long been accepted that reading on electronic displays is slow and associated with visual fatigue, this new generation is explicitly promoted for reading. Since research has shown that, compared to reading on electronic displays, reading on paper is faster and requires fewer fixations per line, one would expect differential effects when comparing reading behaviour on e-Ink and LCD. In the present study we therefore compared experimentally how these two display types are suited for reading over an extended period of time.
Participants read for several hours on either e-Ink or LCD, and different measures of reading behaviour and visual strain were regularly recorded. These dependent measures included subjective (visual) fatigue, a letter search task, reading speed, oculomotor behaviour and the pupillary light reflex.
Results suggested that reading on the two display types is very similar in terms of both subjective and objective measures.
It is not the technology itself, but rather the image quality that seems crucial for reading. Compared to the visual display units used in the previous few decades, these more recent electronic displays allow for good and comfortable reading, even for extended periods of time.
Figures in this publication
Available from: Thierry Baccino
- "Concerning reading behavior, Shen et al.  found E-ink reader (Sony e-reader) to have higher search accuracy with respect to LCD (Kolin e-reader). Siegenthaler et al. , found no differences between the same E-ink device (Sony e-reader) and LCD (iPad 1st generation), as confirmed by both subjective (VFS - ) and objective measures (eye and reading performance measures). Siegenthaler et al.  showed that iPad 1st generation, under special artificial light conditions, may even provide better legibility than Sony e-reader. "
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ABSTRACT: The mass digitization of books is changing the way information is created, disseminated and displayed. Electronic book readers (e-readers) generally refer to two main display technologies: the electronic ink (E-ink) and the liquid crystal display (LCD). Both technologies have advantages and disadvantages, but the question whether one or the other triggers less visual fatigue is still open. The aim of the present research was to study the effects of the display technology on visual fatigue. To this end, participants performed a longitudinal study in which two last generation e-readers (LCD, E-ink) and paper book were tested in three different prolonged reading sessions separated by - on average - ten days. Results from both objective (Blinks per second) and subjective (Visual Fatigue Scale) measures suggested that reading on the LCD (Kindle Fire HD) triggers higher visual fatigue with respect to both the E-ink (Kindle Paperwhite) and the paper book. The absence of differences between E-ink and paper suggests that, concerning visual fatigue, the E-ink is indeed very similar to the paper.
PLoS ONE 12/2013; 8(12):e83676. DOI:10.1371/journal.pone.0083676 · 3.23 Impact Factor
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ABSTRACT: This preliminary study aims at identifying possible differences in eye movements while reading either dark text on light background or light text on dark background on-screen. To this end, eye movements during reading with two different screen technologies which are dominant in the domain of mobile e-reading devices (i.e. eInk and backlit LED), have been examined for both negative and positive contrast. Results show that for reading on electronic displays, direction of contrast (negative/positive) has no significant influence on central eye movements involved in reading. Therefore, both positive and negative contrasts can be recommended when presenting text to users on-screen and provide good readability. This goes for either eInk or LED screens, however, a tendency for longer fixations was observed when reading with negative contrast on the LED screen. This may be due to the higher contrast on the LED screen, compared to the lower contrast of the eInk screen.
Human Factors in Computing and Informatics, Edited by Andreas Holzinger, Martina Ziefle, Martin Hitz, Matjaž Debevc, 01/2013: chapter 51: pages 711-720; Springer Berlin Heidelberg., ISBN: 9783642390616
Available from: Blaz Zupan
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ABSTRACT: For most problems in science and engineering we can obtain data that describe
the system from various perspectives and record the behaviour of its individual
components. Heterogeneous data sources can be collectively mined by data
fusion. Fusion can focus on a specific target relation and exploit directly
associated data together with data on the context or additional constraints. In
the paper we describe a data fusion approach with penalized matrix
tri-factorization that simultaneously factorizes data matrices to reveal hidden
associations. The approach can directly consider any data sets that can be
expressed in a matrix, including those from attribute-based representations,
ontologies, associations and networks. We demonstrate its utility on a gene
function prediction problem in a case study with eleven different data sources.
Our fusion algorithm compares favourably to state-of-the-art multiple kernel
learning and achieves higher accuracy than can be obtained from any single data
IEEE Transactions on Pattern Analysis and Machine Intelligence 07/2013; 37(1). DOI:10.1109/TPAMI.2014.2343973 · 5.78 Impact Factor
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