Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm |
| |
| Affiliation: | 1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China;2. Department of Physical Education, Central South University, Changsha, Hunan, 410083, China;1. Department of Pathology, The Second People''s Hospital of Dongying, Dongying, Shandong, 257000, China;2. Department of Hematology, The Eighth People''s Hospital of Qingdao, Qingdao, Shandong, 266000, China;3. Department of Laboratory, Yaoqiang Street Office Community Health Service Center, Jinan, Shandong, 250000, China;4. Pediatrics, Yaoqiang Street Office Community Health Service Center, Jinan, Shandong, 250000, China;5. Department of Pharmacy, Jinan Hospital of Traditional Chinese Medicine, Jinan, Shandong, 250000, China;1. School of International Education, Heilongjiang University of Technology, Jixi, Heilongjiang, 158100, China;2. School of Electrical and Information Engineering, Heilongjiang University of Technology, Jixi, Heilongjiang, 158100, China;1. School of Sports and Health, Tianshui Normal University, Tianshui, Gansu, 741000, China;2. College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi, 541004, China |
| |
| Abstract: | Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
