File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TNSRE.2020.3016747
- Scopus: eid_2-s2.0-85092507365
- PMID: 32795970
- WOS: WOS:000578017200022
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Dynamic Bayesian Adjustment of Dwell Time for Faster Eye Typing
Title | Dynamic Bayesian Adjustment of Dwell Time for Faster Eye Typing |
---|---|
Authors | |
Keywords | Eye tracking human computer interaction eye typing dwell time motor disability |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333 |
Citation | IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, v. 28 n. 10, p. 2315-2324 How to Cite? |
Abstract | Eye typing is a hands-free method of human computer interaction, which is especially useful for people with upper limb disabilities. Users select a desired key by gazing at it in an image of a keyboard for a fixed dwell time. There is a tradeoff in selecting the dwell time; shorter dwell times lead to errors due to unintentional selections, while longer dwell times lead to a slow input speed. We propose to speed up eye typing while maintaining low error by dynamically adjusting the dwell time for each letter based on the past input history. More likely letters are assigned shorter dwell times. Our method is based on a probabilistic generative model of gaze, which enables us to assign dwell times using a principled model that requires only a few free parameters. We evaluate our model on both able-bodied subjects and subjects with a spinal cord injury (SCI). Compared to the standard dwell time method, we find consistent increases in typing speed in both cases. e.g., 41.8% faster typing for able-bodied subjects on a transcription task and 49.5% faster typing for SCI subjects in a chatbot task. We observed more inter-subject variability for SCI subjects. |
Persistent Identifier | http://hdl.handle.net/10722/305867 |
ISSN | 2023 Impact Factor: 4.8 2023 SCImago Journal Rankings: 1.315 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | PI, J | - |
dc.contributor.author | Koljonen, PA | - |
dc.contributor.author | Hu, Y | - |
dc.contributor.author | SHI, BE | - |
dc.date.accessioned | 2021-10-20T10:15:28Z | - |
dc.date.available | 2021-10-20T10:15:28Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, v. 28 n. 10, p. 2315-2324 | - |
dc.identifier.issn | 1534-4320 | - |
dc.identifier.uri | http://hdl.handle.net/10722/305867 | - |
dc.description.abstract | Eye typing is a hands-free method of human computer interaction, which is especially useful for people with upper limb disabilities. Users select a desired key by gazing at it in an image of a keyboard for a fixed dwell time. There is a tradeoff in selecting the dwell time; shorter dwell times lead to errors due to unintentional selections, while longer dwell times lead to a slow input speed. We propose to speed up eye typing while maintaining low error by dynamically adjusting the dwell time for each letter based on the past input history. More likely letters are assigned shorter dwell times. Our method is based on a probabilistic generative model of gaze, which enables us to assign dwell times using a principled model that requires only a few free parameters. We evaluate our model on both able-bodied subjects and subjects with a spinal cord injury (SCI). Compared to the standard dwell time method, we find consistent increases in typing speed in both cases. e.g., 41.8% faster typing for able-bodied subjects on a transcription task and 49.5% faster typing for SCI subjects in a chatbot task. We observed more inter-subject variability for SCI subjects. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333 | - |
dc.relation.ispartof | IEEE Transactions on Neural Systems and Rehabilitation Engineering | - |
dc.rights | IEEE Transactions on Neural Systems and Rehabilitation Engineering. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Eye tracking | - |
dc.subject | human computer interaction | - |
dc.subject | eye typing | - |
dc.subject | dwell time | - |
dc.subject | motor disability | - |
dc.title | Dynamic Bayesian Adjustment of Dwell Time for Faster Eye Typing | - |
dc.type | Article | - |
dc.identifier.email | Koljonen, PA: kpa229@hku.hk | - |
dc.identifier.email | Hu, Y: yhud@hku.hk | - |
dc.identifier.authority | Hu, Y=rp00432 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNSRE.2020.3016747 | - |
dc.identifier.pmid | 32795970 | - |
dc.identifier.scopus | eid_2-s2.0-85092507365 | - |
dc.identifier.hkuros | 328183 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | 2315 | - |
dc.identifier.epage | 2324 | - |
dc.identifier.isi | WOS:000578017200022 | - |
dc.publisher.place | United States | - |