AI and IoT based Home Automation System
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| Titel: | AI and IoT based Home Automation System |
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| Autoren: | Ashish S. Gawali, Rupali S. Kalokar, Monali M. Jawanjal, Tanushri S. Dharmik, Gaurav H. Bakade, Chinmayee S. Deotare, Sushmit S. Gaikwad |
| Verlagsinformationen: | IJAITE |
| Publikationsjahr: | 2025 |
| Bestand: | Zenodo |
| Schlagwörter: | Internet Of Things Microcontroller, HTTP protocols, Relay, Jumper Wires |
| Beschreibung: | This paper aims to develop a smart home automation system that enables users to control electronic devices using hand gestures. By leveraging computer vision and deep learning techniques, a webcam captures hand gestures, which are processed using a Convolutional Neural Network (CNN) model for real-time classification. The recognized gestures are then transmitted to an ESP8266 microcontroller via Wi-Fi, triggering the corresponding on/off commands for connected IoT devices. This system enhances touchless interaction, offering an intuitive and accessible solution for smart home automation, particularly benefiting individuals with mobility impairments. The project integrates Open CV for image pre-processing, Tensor Flow/Keras for model training, and HTTP protocols for seamless communication between the Python application and the ESP8266. |
| Publikationsart: | article in journal/newspaper |
| Sprache: | English |
| ISSN: | 2455-6491 |
| Relation: | https://zenodo.org/records/15425539; oai:zenodo.org:15425539; https://doi.org/10.5281/zenodo.15425539 |
| DOI: | 10.5281/zenodo.15425539 |
| Verfügbarkeit: | https://doi.org/10.5281/zenodo.15425539 https://zenodo.org/records/15425539 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Dokumentencode: | edsbas.8551683B |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | This paper aims to develop a smart home automation system that enables users to control electronic devices using hand gestures. By leveraging computer vision and deep learning techniques, a webcam captures hand gestures, which are processed using a Convolutional Neural Network (CNN) model for real-time classification. The recognized gestures are then transmitted to an ESP8266 microcontroller via Wi-Fi, triggering the corresponding on/off commands for connected IoT devices. This system enhances touchless interaction, offering an intuitive and accessible solution for smart home automation, particularly benefiting individuals with mobility impairments. The project integrates Open CV for image pre-processing, Tensor Flow/Keras for model training, and HTTP protocols for seamless communication between the Python application and the ESP8266. |
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| ISSN: | 24556491 |
| DOI: | 10.5281/zenodo.15425539 |