College of Physical Education and Health, Jiangxi University of Traditional Chinese Medicine, Nanchang 330000, China.
J Healthc Eng. 2021 Jul 13;2021:1579746. doi: 10.1155/2021/1579746. eCollection 2021.
The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.
定期进行身体活动识别和监测与医疗保健益处相关，这在几项研究中都得到了考虑。定期识别和监测健康状况有助于管理和降低多种疾病（如心血管疾病、糖尿病和肥胖症）的风险。人们可以在医院使用医疗设备进行定期体检，以检查自己的健康状况。然而，大多数情况下，很难到达特定的医疗环境并使用特殊的医疗设备。在本文中，我们实现了一种基于双向门控循环单元的健康状况识别深度学习框架，通过充分利用智能手机加速度传感器提供的信息来提高识别的准确性。我们构建了一个基于双向门控循环单元的模型，通过门控方法来描述输入加速度信号与输出信息之间的关系。因此，它可以自动检测运动员的健康状况，判断其为健康、亚健康和不健康。最后，我们使用从运动员那里收集的实际数据来评估系统的识别性能。结果表明，所提出的方法可以准确预测运动员的健康状况。
