Implementation of Cloud Based Action Recognition Backend Platform

Abstract

The Internet of Things (IoT) growth are rapidly in various fields such as industry 4.0, smart cities, and smart homes. Implementation of IoT for electronic assistance had been researched to increase the longevity of human life. However, not all IoT implementation as human life assistance provides action recognition monitoring on multiple elderly people, provide information such as real-time action monitoring, and real-time streaming in a mobile application. Therefore, this research intends to create a system that can receive and provide information on each elderly people who registered. The Action Recognition Backend Platform will be working as cloud computing to receive and manage input data from Edge Computing Action Recognition. This platform integrated Deep Learning, Data Analytics, Big Data Warehouse that implemented Extract, Transform, and Load (ETL) methods, communication services with MQTT, and Kafka Streaming Processor. The test result showed that the edge computing action recognition got better model accuracy performance from our last model [1], which can predict with 50,7% accuracy in 0.5 confidence threshold. Moreover, the backend platform had been successfully implemented a simple IoT paradigm and got an average delivery time of MQTT communication at 204ms, for streaming data process took an average delay of 680ms.