Controlling DC motor using eye blink signals based on LabVIEW

Wang, Zhi-Hao; Hendrick, -; Yu-Fan, Kung; Chuan-Te, Chan; Shi-Hao, Lin; Jong, Gwo-Jia

doi:10.1109/iceeie.2017.8328763

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Virtual instruments are the names given to the functions in LabVIEW (VIs). LabVIEW programming was concerned with providing an efficient and convenient environment for code creation, particularly when the user has to interact with the program and see the outcomes [30]- [32].…”

Section: Materials and Methods 21 Softwarementioning

confidence: 99%

Brain signals analysis for sleep stages detection using virtual instrumentation platform

Hadiri

Bahatti²,

Magri³

et al. 2023

IJEECS

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This paper discusses the use of the Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software tool for analyzing brain waveforms (i.e EEG: electroencephalogram) to study sleep stages such as deep sleep, light sleep and so on. The used EEG signals are generated in order to span all sleeping phases. Indeed, a mandatory step of signal processing has been performed, such as sampling, filtering and features extraction. This analysis is carried out with the LabVIEW program, which is a popular virtual instrumentation platform. The EEG signals used in the analysis were obtained from an open-source database and went through several steps, including noise removal, classification and feature extraction. To extract the feature, different filters are employed and the outputs of all filters are compared, leading to a sleep level detection. The simulation results show clearly the performances of this analysis.

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Section: Materials and Methods 21 Softwarementioning

confidence: 99%

Brain signals analysis for sleep stages detection using virtual instrumentation platform

Hadiri

Bahatti²,

Magri³

et al. 2023

IJEECS

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“…Generally, eye movement research is used to make applications for controls such as cursors, DC motors [4], wheelchairs [5]- [7], and security systems [8]. These studies tend to ignore the risks of DES to the eyes if used actively for interaction with monitor-based devices [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

Riandini

Aditya

Wardhani

et al. 2022

IJEEI

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Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tunning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue.

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A Fuzzy Logic-Based LabVIEW Implementation Aimed for the Detection of the Eye-Blinking Strength Used as a Control Signal in a Brain-Computer Interface Application

Rușanu

2022

The 15th International Conference Interdisciplinarity in Engineering

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Controlling DC motor using eye blink signals based on LabVIEW

Cited by 8 publications

References 5 publications

Brain signals analysis for sleep stages detection using virtual instrumentation platform

Brain signals analysis for sleep stages detection using virtual instrumentation platform

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

A Fuzzy Logic-Based LabVIEW Implementation Aimed for the Detection of the Eye-Blinking Strength Used as a Control Signal in a Brain-Computer Interface Application

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