Skeleton-Based Human Activity Recognition of Low-voltage Distribution Room Based on MQTT Protocol and Wireless Sensor Network

For the activity recognition of operators in low-voltage distribution rooms, a human activity recognition system based on the bidirectional gated recurrent unit model (BiGRU) is proposed to realize the skeleton information acquisition by the WiFi Channel State Information (CSI) sensor. The CSI sensor is used to collect the activity data and the Kinect v2 camera is used to capture the skeleton of the human body as annotations of the CSI data. The BiGRU model is employed to effectively extract the characteristics of the data. Aiming at the problem of poor remote communication, the MQTT connection can be established by using Message Queuing Telemetry Transport (MQTT) protocol combined with ESP8266 WiFi serial port communication module is proposed to improve the poor communication situation and carry out better data communication. The system achieves remote intelligent monitoring of the activities of the operator in the low-voltage distribution room.