A Tunable Gain and Bandwidth Low-Noise Amplifier with 1.44 NEF for EMG and EOG Biopotential Signal

Vieira, Rafael; Näf, Fabian; Martins, Ricardo; Horta, Nuno; Lourenço, Nuno; Póvoa, Ricardo

doi:10.3390/electronics12122592

Cited by 5 publications

(3 citation statements)

References 31 publications

(70 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A few studies [ 12 , 13 , 14 ] have focused on the EOG technique, in which the authors focused on eye-movement-related research. In [ 12 ], an overview of the history, basic principles, and applications of EOG is provided, together with some limitations of the EOG technique for eye-movement measurements.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 13 ], the development of a new EOG recording system and its application to the study of eye movements are described. In [ 14 ], a new low-noise amplifier for EOG signals is presented and its performance is compared with those of other amplifier designs. They evaluate the performance of the amplifier in terms of its noise, bandwidth, and distortion and then compare it with other amplifier designs that are commonly utilized for EOG signals.…”

Section: Related Workmentioning

confidence: 99%

“…The benefit of this technique is that a more complete picture of the gaze behavior is obtained. In recent years, several researchers [ 14 , 15 , 16 , 17 , 18 ] have discussed video-based eye-tracking movements by analyzing eye recordings. In [ 16 ], a method for real-time video-based eye tracking using deep learning algorithms that combined CNNs and RNNs to accurately track eye positions in real time was proposed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety

Safarov

Akhmedov

Abdusalomov

et al. 2023

Sensors

View full text Add to dashboard Cite

Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the “Open” and “Closed” states.

show abstract