The Scheme Design of Wearable Sensor for Exercise Habits Based on Random Game

Abstract

The development of random game theory has enabled wearable sensors to obtain actuator evolution in sports exercise, thus the design of user exercise habits during the exercise process has begun to be studied. Conventional devices only focus on automatic adjustment of sports design, with slight shortcomings in personalization. To address this issue, this study added an anchor node localization device to the adaptive search hybrid learning algorithm and analyzed the exercise goals of athletes. At the same time, a semi definite programming method was installed in wearable sensors to achieve the goal of paying attention to the physical condition of athletes. To verify the performance of the fusion device, this study conducted experiments on the Physical dataset and compared it with three models such as Harris Eagle Optimization. The accuracy rates of designing exercise habits schemes for the four devices were 97.4%, 96.5%, 94.7%, and 91.2%, respectively, indicating that the model has the strongest stability. Under the same running time, the energy loss of this model was 0.11kW * h, which performs the best among the four models. When the athletes are different in age, the F1 values of the four devices are 5.9, 4.5, 4.2 and 3.6 respectively. The results indicate that the proposed fusion model has strong robustness and is suitable for designing exercise habit schemes in the evolution of sports exercise actuators.