Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things

With the support of big data and information technology, various sectors such as sports, health, and medical industry can realize the integration and readjustment of the existing resources, which improve the operation efficiency of the industry and tap its huge potential. With the advancement in big data analysis, voice features, and Internet of Things (IoT), personalized health management is becoming the development trend and breakthrough of sports and health industry. The application of big data will tap out the huge potential of the sports and health industry. In this paper, we have used the Mel-requency cepstrum coefficient as the speech feature processing method. When the linear frequency is transformed to the Mel frequency by Fourier transform, the calculation accuracy will decrease with the increase in the frequency, and the low-frequency signal will be retained to improve the anti-noise ability. With further study of the voice feature processing and IoT model of big data’s sports and health management, a vector addition regression was developed to compare the two real scoring features of the processing results that pave the way for further analysis and result evaluation. Through experimental verification, it is proved that the method in this paper can better learn the speech features. At the same time, with the introduction of noise reduction, the big data of speech recognition in sports health management has a stronger robustness and improves the overall system performance.