Accurate, tiny size and low power consumption framework for controlling physiological humans states in smart home systems | ||||
| Journal of Contemporary Technology and Applied Engineering | ||||
| Article 1, Volume 4, Issue 1, June 2025, Page 1-16 PDF (2.4 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jctae.2025.371243.1047 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Wessam Mohamed Fikry  1; Abdel-Fattah A. Heliel 2; RAGAB EL-SEHIEMY2; Nermeen Rezk3
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| 1KafrelSheikh University Faculty of Engineering Electrical Engineering Department | ||||
| 2Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University | ||||
| 3Computer Science and Engineering Department, Faculty of Engineering, Kafrelsheikh University | ||||
| Abstract | ||||
| The goal of smart home systems is to raise people's standard of living. Brain-computer interface (BCI) technology is combined with universal plug-and-play (UPnP) home networking. To enable smart home applications, the tiny size and low power consumption of the modules make them appropriate for these applications. The current work focuses on smart home networks that are directly controlled by physiological states of humans. Additionally, this BCI system monitors the active status using a single electroencephalogram EEG channel achieves the system to able to implement in real time domain. This study suggests using a direct neural network interface to operate laptops and home appliances without hurting the body's muscles. This model implements a novel system prototype that may be expanded upon and incorporated with UPnP home networking for other uses. To enable non-invasive brain signal assessment, brainwave impulses are captured and filtered via BCI. These signals are recorded by the Neuro Sky Mind Wave Headset and then are analyzed. These devices are useful to control that need clear answers because they are like a typical on/off binary system and even have access to Eye Blink. Successful results show that, the suggested mechanism is characterized by low cost, mobility, easy operation, and expandable circuitry, it may be readily integrated into home automation to help the elderly and disabled. | ||||
| Keywords | ||||
| Brain-Computer Interfaces (BCIs); Electroencephalogram (EEG); Machine learning | ||||
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