EyeBoard: A fast and accurate eye gaze-based text entry system

Panwar, Prateek; Sarcar, Sayan; Samanta, Debasis

doi:10.1109/ihci.2012.6481793

Cited by 28 publications

(24 citation statements)

References 16 publications

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“…Frequency of Occurrence (Merino et al, 2012), N-gram (Sharma et al, 2012), (Ghosh, 2011) Regency of Use (Bhattacharya and Laha, 2012), ) Words table probability (Guerrier et al, 2011), (Merino et al, 2010), (Truong et al, 2013) Syntactic Probability Table Prediction not specified (Bhattacharya and Laha, 2012), (Guerrier et al, 2011), (Guerrier et al, 2013), (Guerrier et al, 2013), (Janpinijrut et al, 2011b), (Molina et al, 2009b), (Prabhu and Prasad, 2011), (Topal et al, 2012), (Topal et al, 2012) Organization of keys and symbols Evolutionary Algorithm (Guerrier et al, 2011), (Francis and Johnson, 2011) N-gram , (Ghosh, 2011) Digraph Frequency (Gelormini and Bishop, 2013), (Millet et al, 2009), Single character Frequency (Gelormini and Bishop, 2013), , (Guerrier et al, 2013), (Jain and Bhattacharya, 2010), (Merino et al, 2012), (Millet et al, 2009), (Panwar et al, 2012), (Prabhu and Prasad, 2011), (Topal et al, 2012) Fitts' Law (Faraj et al, 2009a, , (Ghosh, 2011) Fitts' Digraph Model , (Ghosh, 2011), (Jain and Bhattacharya, 2010) Disambiguation methods Disambiguation algorithm (Merino et al, 2010), (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al, 2009b), (Sarafis and Markoulidis, 2010) Multi-tap (Burgbacher and Hinrichs, 2014), …”

Section: Methods and Techniques For Data Entry Optimization Implementmentioning

confidence: 99%

“…Matrix , (Arif and Stuerzlinger, 2013), (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), , (Faraj et al, 2009a), (Faraj et al, 2009a), (Faraj et al, 2009b), (Francis and Johnson, 2011), (Gelormini and Bishop, 2013), , (Ghosh, 2011), (Gizatdinova et al, 2012a), (Gizatdinova et al, 2012b), (Guerrier et al, 2011), (Guerrier et al, 2013), (Guerrier et al, 2013), (Jain and Bhattacharya, 2010), (Janpinijrut et al, 2011b), (Kim et al, 2014), (Klima and Slovacek, 2009), (Kwon et al, 2009), (Merino et al, 2010), (Merino et al, 2012), (Miró-Borrás and Bernabeu-Soler, 2009), (Nicolau et al, 2013), (Panwar et al, 2012), (Phanchaipetch and Nattee, 2012), (Polácek et al, 2012), (Sarafis and Markoulidis, 2010), , (Sharma et al, 2012), (Simathamanand and Piromsopa, 2011), (Truong et al, 2013), (Usakli and Gurkan, 2010), (Varcholik et al, 2012), (Vella and Vigouroux, 2013), (Wu et al, 2014), (Yang et al, 2013) Circular (Prabhu and Prasad, 2011), (Topal et al, 2012) Distribution of symbols Frequency (Bhattacharya and Laha, 2012), (Faraj et al, 2009c), (Francis ...…”

Section: Positioning Of Keysmentioning

confidence: 99%

“…Words per minute (WPM) (Arif and Stuerzlinger, 2013), , (Faraj et al, 2009c), (Faraj et al, 2009a), (Faraj et al, 2009b), (Ghosh, 2011), (Gizatdinova et al, 2012a), (Guerrier et al, 2011), (Hoste and Signer, 2013), (Jain and Bhattacharya, 2010), (Joshi et al, 2011), (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al, 2009a), (Molina et al, 2009b), (Nicolau et al, 2013), (Panwar et al, 2012), (Sarafis and Markoulidis, 2010), , (Sharma et al, 2012), (Topal et al, 2012), (Vella and Vigouroux, 2013) Characters per minute (CPM) (Bhattacharya and Laha, 2012), (Burgbacher and Hinrichs, 2014), (Polácek et al, 2012) Gestures per character (GPC) (Polácek et al, 2012) Keystrokes per character (KSPC) (Miró-Borrás and Bernabeu-Soler, 2009), (Molina et al, 2009a), , (Wu et al, 2014) Total error rate (TER) (Arif and Stuerzlinger, 2013), (Biolchini et al, 2007) Minimum string distance (MSD) (Gizatdinova et al, 2012a), (Nicolau et al, 2013) use four keys. The number of keys may increase, but is limited to the number of symbols in the alphabet.…”

Section: Data Entry Performance Metrics Workmentioning

confidence: 99%

See 2 more Smart Citations

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

et al. 2016

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Introduction: Due to the increasing popularization of computers and the internet expansion, Alternative and Augmentative Communication technologies have been employed to restore the ability to communicate of people with aphasia and tetraplegia. Virtual keyboards are one of the most primitive mechanisms for alternatively entering text and play a very important role in accomplishing this task. However, the text entry for this kind of keyboard is much slower than entering information through their physical counterparts. Many techniques and layouts have been proposed to improve the typing performance of virtual keyboards, each one concerning a different issue or solving a specific problem. However, not all of them are suitable to assist seriously people with motor impairment. Methods: In order to develop an assistive virtual keyboard with improved typing performance, we performed a systematic review on scientific databases. Results: We found 250 related papers and 52 of them were selected to compose. After that, we identified eight essentials virtual keyboard features, five methods to optimize data entry performance and five metrics to assess typing performance. Conclusion: Based on this review, we introduce a concept of an assistive, optimized, compact and adaptive virtual keyboard that gathers a set of suitable techniques such as: a new ambiguous keyboard layout, disambiguation algorithms, dynamic scan techniques, static text prediction of letters and words and, finally, the use of phonetic and similarity algorithms to reduce the user's typing error rate.

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Section: Methods and Techniques For Data Entry Optimization Implementmentioning

confidence: 99%

Section: Positioning Of Keysmentioning

confidence: 99%

Section: Data Entry Performance Metrics Workmentioning

confidence: 99%

See 1 more Smart Citation

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

et al. 2016

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“…Intrusive systems, understandably has the potential of giving high accuracy due to the various devices that need to be attached to the user (e.g [21,22]). However, their applications in real world situations might be limited and in some cases, impractical.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, considerable attention has been given to EGT due to its potential applications in fields such as HCI [1][2][3][4] and human cognitive studies (HCS) [5][6][7][8][9][10]. Tremendous efforts have been devoted to nonintrusive [11][12][13][14][15][16][17][18][19][20] as well as intrusive EGT techniques [21][22][23] in order to develop more accurate, faster, and easier-to-use systems. Nonintrusive methods have been much preferred over its intrusive counterpart due to the potential comfort when performing EGT.…”

Section: Introductionmentioning

confidence: 99%

Nonintrusive Eye Gaze Tracking using a single eye image

Banaeeyan

Halin

Bahari

2015

2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

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This paper proposes an Eye Gaze Tracking (EGT) technique using a single eye image that can be easily calibrated and mapped for Human Computer Interaction (HCI). The technique employs both geometric and trigonometric relationships to find a user's Point of Regard, followed by calculating user-dependent variables for final mapping onto a user interface (UI). Experimental results show acceptable accuracy with minimal focal errors.

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Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Niu

Zuo

Yang

et al. 2021

Hum Ftrs & Erg Mfg Svc

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To investigate a suitable design of interactive objects in the fixation-triggered eyecontrol interface, this study conducted two ergonomic experiments based on the location of an object, area of the object, and distance between adjacent objects. Experiment 1 investigated the priority distribution of different regions and recommended values of object sizes in different regions. The results showed that middle-middle (MM) area is the optimal area, while middle-left (ML) area, middleright (MR) area, and upper-left (UL) area are suboptimal areas, and the remaining are general areas. The recommended minimum sizes for the optimal area, suboptimal area, and general area are 2.636°(200 px on the screen of a 1920 px × 1080 px 14 inch monitor), 2.978°(225 px on the screen of a 1920 px × 1080 px 14 inch monitor) and 3.322°(250 px on the screen of a 1920 px × 1080 px 14-inch monitor), respectively. Experiment 2 conducted ergonomic experiments of different distances between adjacent objects in horizontal and vertical distributions.The results showed that the appropriate setting of the distribution and size of the objects showed no significant difference between different levels of spacing on the trigger accuracy rate and task completion time. Further, 1.032°(75 px on the screen of a 1920 px × 1080 px 14-inch monitor) could be recommended as the most suitable spacing. According to the conclusions of Experiments 1 and 2, an optimized eye-control interface and the NASA-TLX scale were used to verify the validity and scientificity of the experimental results. The conclusions can be employed to improve the interaction efficiency of the eye-control system and user experience and expand the application field of eye control.

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EyeBoard: A fast and accurate eye gaze-based text entry system

Cited by 28 publications

References 16 publications

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

A new concept of assistive virtual keyboards based on a systematic review of text entry optimization techniques

Nonintrusive Eye Gaze Tracking using a single eye image

Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

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