文献类型：期刊文献

英文题名：Sports Injury Risk Assessment Based on Blockchain and Internet of Things

作者：Liu, Jihua[1]

通讯作者：Liu, JH[1]

机构：[1]Beijing Union Univ, Dept Phys Educ, Beijing 100101, Peoples R China

第一机构：北京联合大学体育部、体委

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Phys Educ, Beijing 100101, Peoples R China.|[1141790]北京联合大学体育部、体委;[11417]北京联合大学;

年份：2021

卷号：2021

外文期刊名：JOURNAL OF SENSORS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000721108000005)】；

基金：AcknowledgmentsThis work is supported by Youth Fund for Humanities and Social Sciences Research of the Ministry of Education of China (Grant no. 19YJC890019).

语种：英文

外文关键词：Blockchain - Blood - Blood pressure - Computation theory - Decision theory - Network architecture - Physiological models - Risk assessment - Sensor data fusion - Sports - Wearable sensors

摘要：With the increase of people's exercise in today's society, how to exercise scientifically and healthily has attracted much attention. Therefore, sports injury risk assessment and monitoring system has attracted more and more attention in real-time, flexibility, intelligence, and other aspects. To solve the above problems, this paper proposes a sports injury risk assessment based on blockchain and Internet of Things. By introducing computational power weight, a computational power balance D-H algorithm based on Internet of Things blockchain network architecture is proposed. It can provide a secure and trusted interactive environment for the Internet of Things. On the basis of blockchain and Internet of Things, a multisensor data fusion algorithm is proposed to be applied to the analysis and evaluation of sports injury. A variety of physiological parameters of human motion state are collected through multisensor, the collected physiological parameters are processed by data fusion, and finally, sports injury risk assessment is carried out. The built system takes the embedded esp8266wifi module as the hardware processing core and uses body temperature sensor, blood pressure sensor, EMG sensor, and pulse sensor to form wearable devices. By wearing wearable devices, four human physiological parameters such as body temperature, blood pressure, electromyography, and pulse can be collected. In the process of decision level fusion, different weights are set for the focal elements causing information conflict, and the optimized D-S evidence theory algorithm is used. Thus, according to the data detected by multisensor, the injury risk of user motion state is evaluated.