Design of Brain Controlled Robotic Car using Raspberry Pi

Amareswar, Enjeti; Naik, M Raju; Prasad, S. V. S.; Chandra, Navin; Karthik, R.

doi:10.1109/icoei51242.2021.9452957

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Algunas de las aplicaciones de vanguardia comprenden los robots de vigilancia, los robots de múltiples utilidades/multifuncionalidad, los robots de seguimien t o de línea, los robots de detección de objetos y evitación de obstáculos, la detección de enfermedades de cultivos, el servicio de supermercados, el mapeo de túneles, Robot pulverizador, robot de pintura mural, robot de rescate, robot extintor de incendios. Otras aplicaciones, como un automóvil robótico aplicados en la población discapacitada en asistencia del cuidado [11]. Además, en [12] se presenta un robot para ayudar a personas paralíticas y discapacitadas med ian t e el análisis de señales utilizando esta tecnología.…”

Section: Materiales Y Métodosunclassified

Design of Assistant Robot for Monitoring in Health Centers in the Emergency Area

Mendoza¹,

Sanchez²,

Perez³

2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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Due to the lack of equipment to monitor the status of patients in the Intensive Care Units (ICU) in Health Centers, there are patients who enter the Emergency Area every day at any time in search of medical attention. They form queues outside the hea lth centers, waiting for a quick response to determine if it is Urgent or Emergency. The wait they do in the queues requires a control and taking of their vital signs to order and monitor patients for Urgency or Emergency. Patients who are critical, at risk, in a coma, patients with terminal illness and who require care and life expectancy are perceived. Noting this scenario, the objective of this study is to design a robot that provides a solution to monitor patients, thus avoiding crowds. The methodology has been applied in a structured way using engineering principles to perform the system analysis. The results show that such a robot can send patient data and vital signs by text messages (SMS), using the Internet of Things and embedded systems. It is concluded that it is necessary to inno va te with technology to organize and avoid crowds, as well as to monitor vital signs through a system that offers reliability and viability.

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Section: Materiales Y Métodosunclassified

Design of Assistant Robot for Monitoring in Health Centers in the Emergency Area

Mendoza¹,

Sanchez²,

Perez³

2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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“…The Raspberry Pi has also been used for projects based on EEG signals. For example, in [ 12 ], EEG is used to control a car, and, in [ 13 ], it is used to monitor the depth of anesthesia. The purpose of this work is to develop a first approximation to a fully functional portable system for subject identification that uses trained DL models to process the signals.…”

Section: Introductionmentioning

confidence: 99%

Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System

Benomar

Cao

Vishwanath

et al. 2022

Sensors

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Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.

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Revisión sistemática de aplicaciones de robótica para atención de víctimas

Espitia Cubillos,

Jiménez Moreno

2024

RCTA

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Este documento muestra un análisis de los resultados de búsqueda de literatura sobre sistemas tecnológicos de apoyo a personas víctimas de diversos eventos que implican perdida de movilidad en algún grado, requiriendo de desarrollos como la robótica asistencial. Se usa la guía PRISMA 2020 como referente de la revisión sistemática. Se consultó en la base de datos Scopus publicaciones sobre robótica y víctimas de la violencia de los últimos tres años, se construyeron redes bibliométricas con Vosviewer, se filtraron los resultados, se caracterizaron y se agruparon por la atención a víctimas tras desastres y enfermedades. Se concluye, es necesario encaminar los esfuerzos en robótica hacia la atención de víctimas de la violencia en Colombia apoyado en los avances identificados documentados a nivel científico dada la ausencia de evidencia al respecto.

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Design of Brain Controlled Robotic Car using Raspberry Pi

Cited by 3 publications

References 11 publications

Design of Assistant Robot for Monitoring in Health Centers in the Emergency Area

Design of Assistant Robot for Monitoring in Health Centers in the Emergency Area

Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System

Revisión sistemática de aplicaciones de robótica para atención de víctimas

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