Research on dance action recognition and health promotion technology based on embedded systems

Abstract This article aims at presenting a novel framework for dance action recognition and health enhancement. It uses embedded systems with sensors and machine learning. System makes use of an array of inertial measurement units. Also, accelerometers and gyroscopes collect motion data across multiple dimensions, thereby capturing the dynamics of dance movements. This is to recognize the dance actions effectively. And as a solution to the temporal and spatial data dependencies, the convolutional neural network-long short-term memory-based model is utilized. Feature extraction and optimization is performed via a built-in data preprocessing module. This model gives an added advantage of efficiency and low latency. An health promotion module includes biometric tracking and awareness. It tracks and displays data about the dancer’s heart rate, energy consumption, and joint loading. Several experiments are performed to assess the system performance, using various datasets and comparisons with the traditional approaches. Outcome shows enhancement in the recognition rate, energy consumption, and feedback efficiency. This work benefits the development of intelligent dance systems. It also has implications for the application of embedded systems in sports training, rehabilitation, and health monitoring.