FlowBeat: Gesture-Based Control Technique for Intelligent Music Interaction System

Gesture-controlled interfaces offer an intuitive and touchless way to interact with digital music systems. With rapid advancements in computer vision and machine learning, gesture recognition now enables users to play, pause, change tracks, and adjust volume through simple hand movements detected via a webcam. This survey presents FlowBeat, a gesture-based music interaction model that analyzes current recognition technologies, research gaps, system limitations, and user acceptance. The study highlights issues such as lighting sensitivity, gesture ambiguity, latency, and hardware dependencies while comparing sensor-based, machine-learning, and deep-learning recognition methods. The proposed FlowBeat framework uses MediaPipe/OpenCV for real-time gesture classification and offers a cost-effective, accessible, and hands-free media control solution. The findings support the future integration of gesture-controlled interfaces into daily-use smart environments.