Effective Human Activity Recognition through Accelerometer Data

In recent years, the field of Human Activity Recognition (HAR) has emerged as a prominent area of research. A plethora of methodologies have been documented in the literature, all with the objective of identifying and analyzing human activities. Among these, the use of a body-worn accelerometer to collect motion data and the subsequent application of a supervised machine learning approach represents a highly promising solution, offering numerous benefits. These include affordability, comfort, ease of use, and high accuracy in recognizing activities. However, a significant challenge associated with this approach is the necessity for performing activity recognition directly on a low-cost, low-performance microcontroller. This research presents the development of a real-time human activity recognition system. The system employs optimized time windows for each activity, a comprehensive set of differentiating features, and a straightforward machine learning model. The efficacy of the proposed system was evaluated using both publicly available datasets and data collected in experiments, achieving an exceptional activity recognition rate of over 95.06%. The system is capable of recognizing six fundamental daily human activities: standing, sitting, jogging, walking, going downstairs, and going upstairs.