Smart Home Control for Disabled Using Brain Computer Interface

Qin, Li Yi; Nasir, Noorhamizah Mohamed; Huq, M. S.; Ibrahim, Bashar; Narudin, Siti Khadijah; Ghani, Muhamad Amin Ab; Alias, Norlidah

doi:10.30880/ijie.2020.12.04.008

Cited by 13 publications

(15 citation statements)

References 14 publications

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“…A Bluetooth module is used to create communication contacts. Qin et al (Qin et al, 2020) developed a wireless EEG-based BCI smart home control system for disabled people that is portable, cost-effective, and real-time. A prototype device is being created utilizing a Raspberry Pi 3 Model B+, a 4 channel 5V relay module, a light bulb, and a fan.…”

Section: Systems Without ML Classified By Command Typementioning

confidence: 99%

Smart Homes for Disabled People: A Review Study

Ibrahim

Hassan

Ali

2022

SJUOZ

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The field of smart homes has gained notable attention from both academia and industry. The majority of the work has been directed at regular users, and less attention has been placed on users with special needs, particularly those with mobility problems or quadriplegia. Brain computer interface has started the mission of helping people with special needs in smart homes by developing an environment that allows them to make more independent decisions. This study investigates the efforts made in the literature for smart homes that have been established to manage and control home components by disabled people and makes a comparison between the reviewed papers, in terms of the controlled devices, the central controller, the people with disabilities the system is meant for, whether or not machine learning was used in the system, and the system's command method. In the field of machine learning-based smart homes for disabled people, the limitations have been pointed out and talked about. Current challenges and possible future directions for further progress have also been given.

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Section: Systems Without ML Classified By Command Typementioning

confidence: 99%

Smart Homes for Disabled People: A Review Study

Ibrahim

Hassan

Ali

2022

SJUOZ

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“…Many papers released were related to the case when the brain and eye control of immobilized people work normally and can be employed to manage the smart home. The BCI with EEG non-invasive electrodes and a headset installation is broadly applied to manage smart home appliances using IoT techniques [4][5][6][7][8][9][10][11][12][13][14][15][16]. It is also important to personalize smart home interactions to help disabled people improve their daily life routines through solutions based on AI.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The EEG signals are transmitted via Bluetooth to the HC-05 module connected to the Arduino Mega board. The information about edge IoT devices [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] is pre-sented to the disabled on the LCD 1602 display with an IIC adapter wired to the same Arduino Mega microcontroller. The double blink is used to detect an event where the disabled person chooses the option shown on display.…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of the concepts and soft-/hardware solutions presented in [4][5][6][7][8][9][10][11][12][13][14][15][16] shows that none of them implement WBCI to control smart home appliances using IoT data protocol(s) along with budget EEG-based brain kit(s). The perspective approach is the WBCI based on the low-cost brainwave EEG sensor such as Sichiray TGAM and lightweight publish/subscribe IoT data protocol such as MQTT.…”

Section: Introductionmentioning

confidence: 99%

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Prototyping Smart Home for Immobilized People: Eeg/MQTT-Based Brain-to-Thing Communication

Zubov

Qureshi²,

Köse³

et al. 2022

RIC

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Context. Immobilized people face additional barriers in almost all areas of life, including simple operations like turning the light on/off and controlling the air conditioner. The object of the study was to develop the brain-to-thin communication of affordable priceto control the smart home appliances by immobilized people from neck to toes. Objective. The goal of the work is to manage smart home appliances via brain-to-thing communication with EEG non-invasive electrodes, edge IoT devices, and MQTT protocol if the brain and eye control of the disabled work normally. Method. A non-invasive Sichiray TGAM brainwave EEG sensor kit captures signals and then transmit them via Bluetooth to the HC-05 module connected to the Arduino Mega microcontroller. Information about edge IoT devices is presented to the disabled on the LCD 1602 display wired to the same Arduino Mega. The disabled person chooses the option shown on display via the double blink that is detected if the quality of signal equals zero and low/mid gamma waves are less than ten in three consecutive Bluetooth packets. Control commands are sent from Arduino Mega (MQTT publisher) to the edge IoT devices (MQTT subscribers) that analyze them and start a specific operation like opening a door and turning the alarm on/off. Results. Five females and five males of different ages from 8 to 59 years old examined the control of smart home appliances with the Sichiray TGAM brainwave sensor kit. Everyone successfully handled the Sichiray headset and showed satisfaction with the brain-to-thing system. Conclusions. In this work, a smart home concept for immobilized people was developed using the brain-to-thing approach and the MQTT communication between the MQTT publisher, Sichiray TGAM brainwave EEG sensor kit connected via Bluetooth to the Arduino Mega microcontroller, and edge IoT devices total priced at USD 150. The most likely prospect of the presented work is to produce the sample that is ready to market.

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“…Also, many researchers have presented BCI systems in the context of "smart" homes for controlling home appliances. [34][35][36][37][38][39][40][41][42][43] The development of BCI systems that would allow disabled people to control almost every device in their home-from a wheelchair to any electronic appliance-would have a tremendous effect on their life quality. In this paper, we present a case study, as a proof of concept, for an elevator BCI system that could be part of a complete "smart" home BCI system.…”

Section: The Conceptmentioning

confidence: 99%

Residential buildings with brain-computer interface functionality: An elevator case study

Chatziparasidis¹,

Sfampa²

2021

Building Services Engineering Research and Technology

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Brain–computer interfaces (BCI) are systems that use signals recorded from the brain to enable communication and control applications. One of the most important applications of BCI technology is that enables people who are severely paralyzed by amyotrophic lateral sclerosis, brainstem stroke, or other disorders to communicate, operate computer programs, or even control numerous devices. Moreover, elevators are probably the best option for disabled persons to expand their access and mobility within a house or a building. In this study, a prototype application is presented, together with an experimental setup of a BCI system that attempts to control an elevator. Practical application Many researchers are dealing with BCI systems that give the possibility to disabled people to control a variety of devices from wheelchairs to different home appliances, using the signals of their brain and forming a smart home services framework. This work comes to support this effort by presenting a case study, as a proof of concept, for an elevator BCI system that could be part of a complete “smart” home BCI system. The presented experimental setup proves that elevators with BCI functionalities are practically feasible and in an affordable cost, and that they could be a significant element within a “smart” residential building.

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Smart Home Control for Disabled Using Brain Computer Interface

Cited by 13 publications

References 14 publications

Smart Homes for Disabled People: A Review Study

Smart Homes for Disabled People: A Review Study

Prototyping Smart Home for Immobilized People: Eeg/MQTT-Based Brain-to-Thing Communication

Residential buildings with brain-computer interface functionality: An elevator case study

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