Application of temperature sensors and edge detection algorithms optimized based on thermal energy consumption in dance posture training

In recent years, with the development of sensor technology and edge computing, new possibilities for dance posture training have been provided. The aim of this study is to develop a temperature sensor and edge detection algorithm based on thermal energy consumption optimization to improve the efficiency and accuracy of dance pose training. Wearable devices containing high-precision temperature sensors are able to monitor temperature changes in various parts of the dancer's body in real time. Through wireless transmission technology, this data is sent to edge computing devices for processing. Edge computing devices use edge detection algorithms to quickly analyze a dancer's thermal consumption patterns and identify which postures or movements are causing excessive energy consumption. The results show that the temperature sensor and the edge detection algorithm can effectively monitor and analyze the dancers' thermal energy consumption. In the experiment, dancers trained with the system were able to get real-time feedback about changes in their body temperature, so that they could adjust their movements and training intensity in time. Compared with traditional training methods, dancers using this system have shown significant improvement in the optimization of energy consumption, training efficiency and effect have been improved, not only improve the scientific and accurate training, but also provide real-time feedback for dancers to help them manage their energy consumption more effectively.