cost-effective markerless hand gesture classification for intuitive interaction in smart home

In human-computer interaction, some efficient management of intelligent systems hinges on precise human presence detection and his hand gesture recognition. this paper presents a solution leveraging advanced image and signal processing techniques to develop a cost-effective, intelligent hardware system. this approach enhances system intelligence, enabling interaction through hand gesture commands. the hardware system integrates a passive infrared (pir) motion sensor, camera, usb microphone, raspberry pi2, and microcontroller board. core algorithms govern darkness control, motion detection, and human presence identification. hand gesture recognition, implemented with mediapipe and opencv, provides a user-friendly interface for interaction. by incorporating hand gesture-based interaction, it reduces costs and enhances accessibility, fostering inclusive technology development. this hand markerless technique eliminates barriers and expensive hardware while accurately recognizing gestures and commands. through advanced image processing and machine learning, the system interprets hand gestures without physical markers or specialized equipment, enhancing user experience and reducing costs. this advancement unlocks new possibilities for intuitive human-computer interaction in smart buildings, making systems more accessible, user-friendly, and cost-effective, and facilitating broader adoption and integration of gesture-based interfaces.