Study of LoRaWAN Technology for Activity Recognition

In this paper, we explore LoRaWAN (Long Range Wide Area Network) sensor for human activity recognition. In this research, we want to explore relation between packet loss and activity recognition accuracy from LoRaWAN sensor data. We want to estimate the packet loss amount from realistic sensors. In LoRaWAN technology, the amount of sensor nodes connected with a single gateway have an impact on the performance of sensors ultimate data sending capability in terms of packet loss. By exploring a single gateway, we transfer the LoRaWAN sensor data to the cloud platform successfully. We evaluate LoRaWAN accelerometer sensors data for human activity recognition. We explore the Linear Discriminant Analysis (LDA), Random Forest (RnF) and K-Nearest Neighbor (KNN) for classification. We achieve recognition accuracy of 94.44% by LDA, 84.72% by RnF and 98.61% by KNN. Then we simulate the packet loss environment in our dataset to explore the relation between packet loss and accuracy. In real caregiving center, we did experiment with 42 LoRaWAN sensors node connectivity and data transfer ability to evaluate the packet received and packet loss performance with LoRaWAN sensors.