The high potential of wearable physiological sensors in regenerative medicine and continuous monitoring of human health is currently of great interest. In measuring in real-time and non-invasively highly heterogeneous constituents, have a great deal of work and therefore been pushed into creating several sports monitoring sensors. The advanced engineering research and technology lead to the design of a wearable energy-efficient fitness tracking (WE2FT) system for sports person health monitoring application. Instantaneous accelerations are measured against pulses, and specific walking motions can be tracked by this system using a deep learning-based integrated approach of an intelligent algorithm for gait phase detection for the proposed system (WE2FT). The algorithm’s effects are addressed, and the performance has been evaluated. In this study, the algorithm uses a smartphone application to track steps using the Internet of Things (IoT) technology. For this initiative, the central node’s optimal location is measured with the antenna reflectance coefficient and CM3A path loss model (IEEE 802.15.6) among the sensor nodes for energy-efficient communication. The simulation experiment results in the highest performance in terms of energy efficiency and path loss.

中文翻译：

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用于运动员健康监测应用的可穿戴节能健身追踪系统

可穿戴生理传感器在再生医学和持续监测人类健康方面的巨大潜力目前引起了人们的极大兴趣。在实时测量和非侵入性高度异质成分方面，有大量工作，因此被推动创建多个运动监测传感器。先进的工程研究和技术导致设计用于运动员健康监测应用的可穿戴节能健身追踪 (WE2FT) 系统。针对脉冲测量瞬时加速度，并且该系统可以使用基于深度学习的集成方法对所提出的系统（WE2FT）进行步态相位检测的智能算法来跟踪特定的步行运动。解决了算法的影响，并评估了性能。在这项研究中，该算法使用智能手机应用程序来跟踪使用物联网 (IoT) 技术的步数。对于该计划，中心节点的最佳位置是通过传感器节点之间的天线反射系数和 CM3A 路径损耗模型 (IEEE 802.15.6) 来测量的，以实现节能通信。仿真实验在能效和路径损耗方面取得了最高的性能。