Smart mobility solution with multiple input Output interface

Sethi, Aartika; Deb, Sujay; Ranjan, Prabhat; Sardar, Arghya

doi:10.1109/embc.2017.8037680

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The system developed by Sethi and Aartika consists of data collection, input, interface, and output modules [22]. They proposed multiple data inputs for controlling the EPW and introduced the idea of controlling home appliances with EEG signals and Android mobile over the IoT.…”

Section: Motor Imagery (Mi)mentioning

confidence: 99%

A Mental State-Aware Brain Computer Interface for Adaptive Control of Electric Powered Wheelchair

Hussain,

Raza,

Hussain

et al. 2024

Preprint

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Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance, while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively tunes toward patient’s psychological effects, could provide the foundation for refining the BCI applications. This paper focuses on the collection and realization of the electroencephalogram (EEG) signals data of humans, obtained as a response to different psychological effects of sound stimuli. Filtration and preprocessing of the dataset are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26\%. Moreover, an automated BCI system is developed to control an electric-powered wheelchair (EPW) while responding to the user’s mental state with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and emotion-responding for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 seconds to generate the interpretable brain signal from the user.

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Section: Motor Imagery (Mi)mentioning

confidence: 99%

A Mental State-Aware Brain Computer Interface for Adaptive Control of Electric Powered Wheelchair

Hussain,

Raza,

Hussain

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…They does not produce any heat in the interior and exterior surfaces. Due to the absence of brushes, it does not tend to create mechanical damage to the motors [20]- [22]. This helps to protect the motor from reducing the winding failure.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Automatic Speed Control of Ceiling Fan through PWM Technique with Optocoupler to Reduce Energy Consumption

Satish,

Sandeep,

Kapili

et al. 2023

Recent Trends in Data Science and Its Applications

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The evolution of automation and innovations helps in diverse field. This helps to create human lives much easier. It tend to produce various automatic devices. These devices must include the control system to improve the automation in the equipment. The important equipment used in our day to day life is the ceiling fan. This is the largely used equipment. The conventional system includes manual speed control. To enhance automation in the system, the proposed system is implemented to replace the conventional system with automatic speed control system. This helps to automatically control the speed of the fan based upon the surrounding temperature. This is sensed through the use of sensors. This includes pulse width modulation techniques with brushless permanent magnet DC motors and microcontroller. Here PIC microcontrollers are used for controlling and processing. Thus these techniques are employed for the automatic detection of ceiling fan. This leads in the reduction of energy consumption and reduced cost.

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Improved Pattern Recognition Techniques for Monitoring Human Activity Recognition in Digital Platforms through Image Processing Techniques

Sumathi,

Vanathi,

Musale

et al. 2023

2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)

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Smart mobility solution with multiple input Output interface

Cited by 3 publications

References 8 publications

A Mental State-Aware Brain Computer Interface for Adaptive Control of Electric Powered Wheelchair

A Mental State-Aware Brain Computer Interface for Adaptive Control of Electric Powered Wheelchair

Design and Implementation of Automatic Speed Control of Ceiling Fan through PWM Technique with Optocoupler to Reduce Energy Consumption

Improved Pattern Recognition Techniques for Monitoring Human Activity Recognition in Digital Platforms through Image Processing Techniques

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