Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

Fang, Shao C.; Hussein, Ahmed Faeq; Ramkumar, S.; Dhanalakshmi, K.; Emayavaramban, G.

doi:10.1109/access.2019.2899188

Cited by 28 publications

(7 citation statements)

References 38 publications

(29 reference statements)

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“…During the classification, we normalized the samples between zero and one using the normalization procedure. The maximum iteration of the network was fixed and limited to 1000, and network error falls below 0.001 to determine the network performance [ 34 , 39 , 40 ]. Figures 2 and 3 present the construction of the ERNN and DTDNN models used in this experiment.…”

Section: Classification Techniquesmentioning

confidence: 99%

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Thilagaraj

Ramkumar

Karthikeyan³

et al. 2021

Journal of Healthcare Engineering

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Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

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Section: Classification Techniquesmentioning

confidence: 99%

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Thilagaraj

Ramkumar

Karthikeyan³

et al. 2021

Journal of Healthcare Engineering

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“…They used the nonlinear regression pattern for analysing the relationship between hospital mortality and the median pulse oximetry‐derived oxygen saturation (SpO 2 ). Furthermore, they derived the optimal SpO 2 range and analysed the relationship between the hospital mortality rate and optimal SpO 2 range using the time factor (%) (Fang et al, 2019).…”

Section: Prediction Of Sepsis Using Clinical Data—related Workmentioning

confidence: 99%

“…The HRV or SpO 2 bio‐signals which are collected are analysed using two phases, that is, linear or non‐linear techniques (Fang et al, 2019; Vidyashree et al, 2019). The linear techniques include parameters which are measured in the frequency or time domains.…”

Section: Introductionmentioning

confidence: 99%

Characterization of heart rate variability and oxygen saturation in sepsis patients

Al-Mualemi

2021

Expert Systems

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One of the most major and common health crises which occur across all the hospitals, worldwide, is seen to be sepsis that occurs in patients. However, despite its wide prevalence no novel tool has been devised for predicting its occurrence. An accurate and early prediction of sepsis in the patients could significantly help the physicians administer proper treatment and decrease the uncertain diagnosis. Some machinelearning-based models or schemes can help in identifying the potential clinical variables and display a better performance compared to the prevailing conventional lowperformance models. In this study, a machine learning-based scheme for fast and accurate sepsis identification was proposed. This scheme employed the power spectrum and mean estimation for data record intervals, which were then classified for reaching the final decision. For a 72-h interval, the obtained detection accuracy was 94.2% that shows very good sign to use it as a fast and robust sepsis identification.

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“…The USBexit from the laptop is taken from the microprocessor and the signal from it is used to control the wheelchair [8]. For the system to function properly, different sensors are connected to the Raspberry Pi [9]. Programming a Raspberry Pi to control a smart wheelchair involves several steps, including, but is not limited to, selection of the appropriate hardware components, setting up the Raspberry Pi, programming the microcontroller, and designing a controller.…”

Section: Introductionmentioning

confidence: 99%

Smart Wheelchair Controlled Through a Vision-Based Autonomous System

Masud,

Abdualaziz Almolhis,

Alhazmi

et al. 2024

IEEE Access

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People are preoccupied with their own duties in today's environment, making it tough to oversee and care for disabled people. Paralyzed and disabled people, on the other hand, have a powerful desire to move around freely. Different initiatives have been taken in the past to improve and preserve the self-esteem of such people, and various technologies have also been created to assist them in a better way. The primary advantage of a vision-based autonomous wheelchair is that it can provide greater independence to users with limited mobility. The methodology of other smart wheelchairs varies depending on the specific technology used. Some smart wheelchairs may use sensors to detect obstacles and provide feedback to the user, while others may incorporate GPS and other navigation technologies to assist with indoor and outdoor navigation. Our study proposes a revolutionary implementation of an autonomous system for fully handicapped people, allowing them to drive wheelchairs using just their eyes. The wheelchair's orientation will be determined by the orientation of the eye. The camera will follow the movement of the eyes. Furthermore, sensors will be installed on the front side of the wheelchair to identify any obstacles along its path. The calibration of the camera with the human eye, without obstructing its vision, is the most challenging task in this research, besides controlling the wheels of the wheelchair.

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Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

Cited by 28 publications

References 38 publications

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Characterization of heart rate variability and oxygen saturation in sepsis patients

Smart Wheelchair Controlled Through a Vision-Based Autonomous System

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