Human Activity Recognition by Edge Computing Based Convolutional Neural Network

Abstract

Human activity recognition has become a hotly debated research topic due to its numerous important and futuristic applications, such as automated surveillance, automated vehicles, language interpretation, and human-computer interfaces. (HCI). Extensive and in-depth research has been conducted, and good progress is been achieved in this area of research. The proposed system is designed by taking health monitoring as an application and implemented on a Raspberry Pi board, which can be used for monitoring and surveillance purposes. The activities considered in our proposed work are standing, jogging, climbing upstairs, and climbing downstairs. The surveillance camera will watch people’s movements to see if they are performing the assigned job. We propose a CNN-based technique to recognize different actions. An alert will sound and the system will report that the person’s designated task has been disrupted if the camera determines that they are engaged in another activity.