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Real-Time Human Activity Recognition System Based on Capsule and LoRa

Human activity recognition (HAR) has become a research hotspot in the field of artificial intelligence and pattern recognition. However, the HAR system still has some deficiencies in the aspects of platform algorithms and wireless access technologies. On the one hand, some state-of-the-art frameworks such as convolutional neural network (CNN) and recurrent neural network (RNN) have been proven successfully in classification tasks of HAR, while those frameworks just identify the feature data of activity but ignore the spatial relationship among features, which may lead to incorrect recognition. On the other hand, some existing transmission modes, such as Bluetooth and 4G, are difficult to realize real-time transmission in the case of a large range and low-power consumption. In this paper, a real-time human activity recognition system based on capsule and “long range” (LoRa) is presented, which pioneers the application of capsule to HAR. The capsule framework encapsulates the multiple convolution layers in parallel to solve the defect that current frameworks cannot identify the spatial relationship among features. Simultaneously, the combination of long-distance transmission and low-power consumption is achieved by using LoRa networking technology instead of other existing transmission modes. The experiments are performed on the dataset WISDM that is collected by the Wireless Sensor Data Mining Lab in Fordham University, and the results demonstrate that the proposed capsule framework achieves a higher classification result than CNN and RNN, and the proposed system makes the real-time HAR based on intelligent sensor devices possible in some special scenarios such as smart prison.