The flexible functional composites have widely potential applications in the fields of athlete health monitoring and auxiliary training. Recently, there are a few reports on various functional composites such as graphene-based composites, MXene-based composites, and polymer-based composites, etc. However, the applications of flexible functional composites for athlete health monitoring and auxiliary training have not widely reviewed yet. This mini-review article summarizes these three types of functional composites for the applications of athlete health monitoring and auxiliary training. The synthetic methods, structures, and properties of functional composites are reviewed via some typical examples. We pay attention to the properties of composites sensor about health-monitoring. Moreover, the directions are suggested based on our knowledge. It demonstrates that these flexible functional composites will display excellent properties and promising applications potential in athlete health monitoring and auxiliary training.

With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. However, the past sports training records of students are often accumulated with time and stored in a central cloud platform and therefore, the data volume is too large to be processed with quick response. In addition, the past sports training records of students often contain certain sensitive information, which probably discloses partial user privacy if we cannot protect the data well. Considering these two challenges, a privacy-aware and efficient student clustering approach, named PESC is proposed, which is based on a hash technique and deployed on a central cloud platform connecting multiple local servers. Concretely, in the cloud platform, each student is firstly assigned an index based on the past sports training records stored in a local server, through a uniform hash mapping operation. Then similar students are clustered and registered in the cloud platform based on the students’ respective sport indexes. At last, we infer the personalized sport preferences of each student based on their belonged clusters. To prove the feasibility of PESC, we provide a case study and a set of experiments deployed on a time-aware dataset.