Relevant studies have shown that the use of sports equipment for exercise can regulate the body composition of patients, thereby achieving the purpose of improving the metabolism of blood lipids in the body. Based on this, this article takes patients with dyslipidemia as the research object and explores the changes in their body composition and shape using different strength sports equipment made of nanomaterials during sports rehabilitation training. In this study, 200 patients with dyslipidemia were selected as experimental subjects in the form of interviews and questionnaires. According to the type of exercise, they were divided into 56 men in the men’s running group, 56 men in the men’s spinning group, 44 women in the women’s running group, and 44 women in the women’s spinning group. 12 weeks of incremental exercise training were carried out. The results of the experiment found that after the end of the 12-week exercise training, the body composition of the subjects in each group changed significantly ( P < 0.05 ). The changes in the male and female spinning group were more obvious than those in the running group, and the weight of the male spinning group, waist circumference, and hip circumference decreased by 2.3%, 3%, and 3.5% and those of women’s spinning group decreased by 3.1%, 3.4%, and 3.9%. In addition, blood lipids in each group also changed significantly ( P < 0.05 ); there is a significant statistical difference. Through a return visit two years after the end of the experiment, it was found that most of the subjects had symptoms of discomfort, indicating that the nanomaterials have a certain negative impact on the human body.

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The task of human motion recognition based on video is widely concerned, and its research results have been widely used in intelligent human-computer interaction, virtual reality, intelligent monitoring, security, multimedia content analysis, etc. The purpose of this study is to explore the human action recognition in the football scene combined with learning quality related multimodal features. The method used in this study is to select BN-Inception as the underlying feature extraction network and use uncontrolled environment and real world to capture datasets UCFl01 and HMDB51, and pretraining is carried out on the ImageNet dataset. The spatial depth convolution network takes image frame as input, and the temporal depth convolution network takes stacked optical flow as input to carry out human action multimodal identification. In the results of multimodal feature fusion, the accuracy of UCFl01 dataset is generally high, all of which are over 80%, and the highest is 95.2%, while the accuracy of HMDB51 dataset is about 70%, and the lowest is only 56.3%. It can be concluded that the method of this study has higher accuracy and better effect in multimodal feature acquisition, and the accuracy of single-mode feature recognition is significantly lower than that of multimodal feature recognition. It provides an effective method for the multimodal feature of human motion recognition in the scene of football or sports.