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LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

机译:智能城市的基于Lora的智能物联网应用:人类姿势检测的一个例子

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Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.
机译:科学家们探索了人体数百年,仍然可以发现行为和健康之间的更多关系。随着数据挖掘,人工智能技术和人类姿势检测的发展,更有可能弄清楚人行为和运动如何影响人们的健康和生活以及如何调整工作与休息之间的关系,这是迫切需要的现代人们反对这种高速生活方式。使用智能技术和日常行为监督或预测人们的健康是智能城市的关键部分。在一个智能城市,这些应用涉及大型组和高频使用,因此系统必须具有低能耗,便携式系统和长期检测的低成本。为满足这些要求,本文提出了一种基于多传感器的姿势识别方法,并使用LORA技术构建长期姿势检测系统。 LORA WAN技术具有低成本和传输距离的优点。结合Lora传输模块和传感器,本文设计可穿戴衣服,让人们在任何特定的姿势中舒适。旨在Lora的低传输频率和小尺寸的数据传输,提出了一种多处理方法,包括数据去噪,基于滑动窗口,特征提取和使用随机林的特征选择的数据放大,使4个值保留有关的最多信息125来自9个传感器轴的数据。结果显示了99.38%的提取功能的精度和95.06%的选定功能,培训3239组数据集。为了验证所提出的算法的性能,三个测试员创建了500组数据集,结果表现出良好的性能。因此,由于LORA的能量可持续性和识别的准确性,这种建议的使用多传感器和LORA的姿势识别在面对长期检测时,在面向长距离传输时,LORA适合智能城市。

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