Tan, Teck Sheng (2024) HandGestureSense: Hand gesture recognition-controlled smart home automation. Final Year Project, UTAR.
Abstract
This project focuses on smart home automation controlled by gesture recognition, integrating computer vision, machine learning and home automation technologies. The main goal is to develop an accurate system that can effectively detect and interpret gestures while enhancing the system's adaptability and performance. The system enables users to seamlessly control every aspect of their smart home without the need for physical contact. The proposed project utilizes computer vision algorithms to capture and analyze live image or gesture video streams. Through extensive training and fine-tuning using machine learning techniques, the system learns to recognize a series of gestures, each corresponding to a different command or function in a smart home environment. These gestures may include actions such as opening, closing, pointer or other hand movements, with a particular focus on controlling and monitoring fan and light appliances. The proposal has important implications for people with limited mobility or disabilities, allowing them to easily navigate and manage their living spaces. Additionally, gesture recognition systems have the potential to improve energy efficiency and safety by responding to gestures indicating a user's presence or absence, thereby adjusting lighting and surveillance systems accordingly.
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