AI-based personalized Human Activity Recognition in walking and trekking sports: A case study

Human Activity Recognition (HAR) is a topic of interest in several areas, for example, health and sports. There are several ways to perform HAR tasks, which must be understood before creating the desired tools. In this article, we carry out a theoretical study to understand the HAR problem and its main techniques using AI. In addition, we present a case study in which we developed a prototype HAR system for walking and trekking. In this study, we evaluated new hardware for HAR with IMUs with 9 degrees of freedom. This system, composed of four sensors called SPUs was attached to the users lower limbs. A device called WPU collects information from these sensors. With the fusion of the collected data, AI techniques were applied with CNN models reaching an accuracy of 98% to classify the human activities in this context, thus creating new perspectives for using AI to HAR in sports.