Ambidextrous Virtual Keyboard Design with Finger Gesture Recognition

Lee, Tae-Ho; Lee, Hyuk-Jae

doi:10.1109/iscas.2018.8351485

Cited by 13 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, distinguishing difference among the easily confused keys through the algorithm can lead to a keyboard layout that is better suitable for this virtual keyboard scheme and reach improved accuracy and comfort. Further improvements will be possible by adjusting the key size, the key position, the layout, and even the shape [40]- [42].…”

Section: Discussionmentioning

confidence: 99%

An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

et al. 2020

View full text Add to dashboard Cite

In our previous work, we developed an IMU (Inertial Measurement Unit) based smart ring that allows users to type characters without a physical keyboard and adopt well-known pattern recognition algorithms, such as Support Vector Machine (SVM), and Naive Bayes (NB), for keystroke recognition. However, these algorithms always require intensive computing resources or offer limited recognition accuracy. Moreover, they are often seen as black boxes incapable of providing readily comprehensible and visible clues for classification. This hampers the improvement of keystroke recognition accuracy and the ring-type virtual keyboard's character layout design. Here we present a novel algorithm to recognize keystrokes in a fast and accurate manner. Firstly, the standard feature vector, including five attitude angle features and one acceleration feature, is built to express a specific stroke. Then, the feature vector of the testing keystroke is compared with the standard features. The most similar keystroke is matched and recognized after three times of voting. Based on this algorithm, we can identify the easily confused keystrokes and understand the mechanisms behind it. With this interpretability, we will be able to achieve the customized ring-type virtual keyboard application if necessary. The performance of this algorithm was evaluated by using a dataset with 1500 keystrokes of three different subjects. The results show that our algorithm is more effective in keystroke recognition than traditional algorithms for this ring-type keyboard. In addition to its application on virtual keyboards, this algorithm can also be potentially applied on other classification tasks with easy-to-understand results.

show abstract

Section: Discussionmentioning

confidence: 99%

An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Based on the research results, it is necessary to carry out further studies based on a qualitative approach using user experience methods such as heuristic evaluation [30] and usability evaluation [31], [32] so that the comparison with static and manual virtual keyboards can be seen. The performance of the system to select the desired menu is 5.1 seconds Directional eye movement detection system for virtual keyboard controller [7] Virtual keyboard Alphabet (static) typing rate on virtual keyboard 25…”

Section: Adaptive Virtual Keyboard Performancementioning

confidence: 99%

Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

View full text Add to dashboard Cite

Rapid technological developments, one of which is technology to build communication relationships between humans and machines using Biosignals. One of them is Electrooculography (EOG). EOG is a type of biosignals obtained from eye movement. Research related to EOG has also developed a lot, especially for virtual keyboard control. Research on virtual keyboard control based on eye gaze motion using electrooculography technology has been widely developed. Previous research mostly drew conclusions based on time consumption in typing paragraphs. However, it has not been seen based on the number of eye gaze motions made by the user. In this research, an adaptive virtual keyboard system is built, controlled using EOG signals. The adaptive virtual keyboard is designed with 7x7 dimensions and has 49 buttons, including main buttons, letters, numbers, symbols, and unused buttons. The layout of the adaptive virtual keyboard has six zones. Each zone has a different number of steps. Characters located in the same zone have the same number of steps. The adaptive feature is to rearrange the position of the character's button based on the previously used characters. In the experiments, 30 respondents controlled static and adaptive virtual keyboards with 7 paragraphs typed. Adaptive mode rearranges the position of buttons based on k-selection activities from respondents. the k numbers are 10, 30, 50, 70 and 100. Two virtual keyboard modes are evaluated based on the number of steps required to type the paragraphs. Test results show that the performance of the adaptive virtual keyboard can shorten the number of user steps compared to static mode. There are tests of the optimal system that can be reduced up to 283 number of steps and from respondents, that can reduced up to 258 number of steps or about 40% of steps. This research underscores the promise of EOG-driven adaptive virtual keyboards, signaling a notable stride in augmenting user interaction efficiency in typing experiences, heralding a promising direction for future human-machine interface advancements.

show abstract

Real Time Palm and Finger Detection for Gesture Recognition Using Convolution Neural Network

Ramya¹,

Srinivasan²

2019

Human Behaviour Analysis Using Intelligent Systems

View full text Add to dashboard Cite

Ambidextrous Virtual Keyboard Design with Finger Gesture Recognition

Cited by 13 publications

References 8 publications

An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

Enhancing Interface Efficiency: Adaptive Virtual Keyboard Minimizing Keystrokes in Electrooculography-Based Control

Real Time Palm and Finger Detection for Gesture Recognition Using Convolution Neural Network

Contact Info

Product

Resources

About