High performance computation of human computer interface for neurodegenerative individuals using eye movements and deep learning technique

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

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Classification of Stages Diabetic Retinopathy Using MobileNetV2 Model

10.29007/h46n ◽

2022 ◽

Author(s):

Hoang Nhut Huynh ◽

Minh Thanh Do ◽

Gia Thinh Huynh ◽

Anh Tu Tran ◽

Trung Nghia Tran

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

High Performance ◽

Vision Loss ◽

Training Model ◽

Average Accuracy ◽

Common Diagnosis ◽

Learning Technique ◽

The Common

Diabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.

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Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

International Journal of the Physical Sciences ◽

10.5897/ijps11.1486 ◽

2012 ◽

Vol 7 (14) ◽

Author(s):

Siriwadee Aungsakun

Keyword(s):

Eye Movements ◽

Computer Interface ◽

Human Computer Interface

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DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2019.101765 ◽

2020 ◽

Vol 102 ◽

pp. 101765 ◽

Cited By ~ 3

Author(s):

Geer Teng ◽

Yue He ◽

Hengjun Zhao ◽

Dunhu Liu ◽

Jin Xiao ◽

...

Keyword(s):

Deep Learning ◽

Computer Interface ◽

Human Computer Interface ◽

Design And Development

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Low power CMOS based Self Controlled Precharge Free Content Addressable Memory with Minimum area for Image Processing Devices

International Journal of Systems Applications, Engineering & Development ◽

10.46300/91015.2020.14.19 ◽

2020 ◽

Vol 14 ◽

Keyword(s):

Image Processing ◽

Low Power ◽

High Speed ◽

High Performance ◽

Search Time ◽

Cmos Technology ◽

Vital Role ◽

Computer Interface ◽

Human Computer Interface ◽

Content Addressable Memory

Image processing devices plays a vital role in several applications like medical, security, biometric etc. The devices ranges from portable size to larger machines with and without Human Computer Interface possibilities. As the image processing and human computer interface system application requires higher memory requirements, the power and area should be small. Searching of data is a high priority work in image classification. To perform high speed search through hardware Content Addressable Memory is used. But the circuit suffers from higher power consumption, precharging issues and low performance. For longer word length the elimination of precharge is needed. So for high speed applications self-controlled precharge-free CAM (SCPF-CAM) is suitable. A 4T hybrid self controlled pre charge free Content Addressable Memory is proposed in this paper using CMOS 32nm technology. The observation shows that the circuit works at high speed, minimizes the search time and has high performance operation. When compared to the conventional SCPF-CAM, 8T CAM the proposed design reduces the number of transistors. The reduction in area is about approximately 20% and can be used in low power and low energy applications. In Synopsis HSPICE Predictive technology models were used for the implementation in 32nm CMOS technology. The work will be extended in future using FinFET technology where the leakage current can be minimized.

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