Edge Computing Framework for Wearable Sensor-Based Human Activity Recognition

Abstract

Human activity recognition is done based on the observation and analysis of human behavior to understand the performed activity. With the emergence of battery powered, low cost and embedded wearable sensors, it became possible to study human activity in various real-world scenarios. Together with the development in data collection, novel machine learning based modeling approaches show huge promise in modeling human activities accurately. Edge computing framework, that is capable of executing human activity recognition models at the edge of the network, is presented in this paper. Framework architecture and its implementation on a single board computer are presented. The framework allows the implementation of various machine learning models for human activity recognition in a standardized manner. The framework is demonstrated experimentally.