EOG based virtual keyboard

Teja, Sai; Embrandiri, Sharat S.; Chandrachoodan, Nitin; Reddy, M. Ramasubba

doi:10.1109/nebec.2015.7117201

Cited by 15 publications

(8 citation statements)

References 3 publications

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“…When the eyes control the Virtual Keyboard via EOG signals, things will be different. In other research, virtual control of a static alphabet-shaped keyboard using EOG signals has been developed [15]. This research shows that the system accuracy was 100%, with an average speed of typing characters of 12 seconds/character [15].…”

Section: Introductionmentioning

confidence: 84%

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

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

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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.

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Section: Introductionmentioning

confidence: 84%

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

Anandika,

Laksono,

Suhaimi

et al. 2023

JNTE

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“…The WIKEY system [29] proposed by K. Ali is a good example, which is established using the change in Channel Status Information (CSI) values of WiFi signals when typing. Also, other schemes such as acoustics-based [30]- [32], electromagnetic emissionsbased [33] and EOG (electrooculography) or EEG (electroencephalogram) based [34]- [36] principles have also been explored to create virtual keyboards.…”

Section: Other Non-touched Virtual Keyboardsmentioning

confidence: 99%

Wireless IoT Motion-Recognition Rings and a Paper Keyboard

et al. 2019

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In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand. INDEX TERMS Wearable sensors, wireless IoT ring, keystroke recognition, virtual keyboard, micro IMU.

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“…A resistor-capacitor (RC) band-pass filter was designed combining a high-pass and a low-pass filter and arranged after the electrodes to attenuate noises that occur in the skin-wireelectrode interface. Considering that the sEMG frequency spectrum ranges from 0 to 400 Hz [14], the low-pass stage of the filter has a cutoff frequency of 338.627 Hz. However, the aforementioned low-frequency noises, such as motion artifact and electrochemical voltage, [14] may contaminate the sEMG signal.…”

Section: ) Filteringmentioning

confidence: 99%

“…Considering that the sEMG frequency spectrum ranges from 0 to 400 Hz [14], the low-pass stage of the filter has a cutoff frequency of 338.627 Hz. However, the aforementioned low-frequency noises, such as motion artifact and electrochemical voltage, [14] may contaminate the sEMG signal. Therefore, a high-pass filter with a cutoff frequency of 7.234 Hz was designed to attenuate such interferences.…”

Section: ) Filteringmentioning

confidence: 99%

Development of an 8 channel sEMG wireless device based on ADS1299 with Virtual Instrumentation

Pires

Júnior

Stevan

2019

J. Appl. Instrum. Control

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In this paper, a different approach on the use of the ADS1299 (an analog front-end with features for electroencephalogram and electrocardiography signal acquisition) is considered, proposing the development of a surface electromyography (sEMG) device. The main features of the device include simultaneous recordings of eight muscular channels, wireless transmission and virtual instrumentation with the use of LabVIEWTM software. The proposed sEMG device contains a specifically designed protocol to accommodate data transmission by reducing the data size while still delivering adequate resolution (34.33 μV), amplitude range (±17.57 mV) and sampling rate (1000 Hz) for sEMG signals. For the validation methods, a generated sine wave and a known sEMG data were evaluated. Moreover, the muscular recordings for all the eight channels of a human arm were successful and the results expose the isolated contractions of the triceps and the biceps with their amplitude range and frequency spectrum.

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EOG based virtual keyboard

Cited by 15 publications

References 3 publications

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

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

Wireless IoT Motion-Recognition Rings and a Paper Keyboard

Development of an 8 channel sEMG wireless device based on ADS1299 with Virtual Instrumentation

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