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

• Main Authors: Jinkun Han, Wei Song, Amanda Gozho, Yunsick Sung, Sumi Ji, Liangliang Song, Long Wen, Qi Zhang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2020-01-01
• Series: Wireless Communications and Mobile Computing
• Online Access: http://dx.doi.org/10.1155/2020/8822555
• ISSN: 1530-8669; 1530-8677

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.