Wearable Sensor-Based Motion Data Analysis and Dance Performance Using Images and Cloud Computing

Abstract

China’s cultural performance career has been developed in recent times, resulting in a large number of students embarking on the professional path of cultural performance. To reduce the teaching pressure on school teachers, dance training gradually introduces the idea of “digital dance”, using sensors to monitor posture. The nonwearable monitoring method does not require contact with the subject and typically relies on images or signal waves for positioning, but it has stringent requirements for the measurement scene. On the other hand, the wearable monitoring method requires the sensor to be worn on the subject’s body all the time, so it needs to be light and energy-efficient. Therefore, this paper designs and implements a set of application platforms, which parametrically analyze the dance movements, consume low energy, are lightweight, and are simple to use. For this purpose, a wearable digital dance training system based on microelectro-mechanical system (MEMS) sensors has been developed, and a solution to the challenges of teaching movement precision, scientific teaching methodologies, and early warning analysis of joint is also proposed. Firstly, the basic knowledge of human posture measurement and human model is merged to build a new modifiable human model. Secondly, the overall design of the posture data acquisition device is presented, which includes a base station and several nodes. Finally, the overall framework of the software platform is designed, which consists of client and server. The experimental results show that the system can achieve the purpose of providing teaching programs for dance students.