Assessment of IMU Configurations for Human Activity Recognition Using Ensemble Learning

Abstract

Populations ranging from elite athletes to the elderly can benefit from unobtrusive activity monitoring. Inertial motion capture, which utilizes inertial measurement units (IMUs), is a relatively inexpensive and common technology that is uniquely advantageous when employed to achieve this goal. This study considers how varying IMU configurations in human activity recognition influences performance metrics. Ensemble learning was used for classification with support vector machine and k-nearest neighbor machine learning models utilized as inputs for a shallow neural network. The accuracy and F1 score for the neural network were compared between the four sensor configurations (full set, necessary only, person only, and tool only). The full set and necessary only performed the best with accuracy and F1 score above 97% and 0.95 respectively. The improved performance of these sets over the person only and tool only showed the benefits of a mixed composition of sensors.

Keywords

• Inertial measurement units
• wearable sensors
• machine learning
• deep learning
• ensemble learning
• activity classification