IMU-Based Robust Human Activity Recognition using Feature Analysis, Extraction, and Reduction

摘要

In recent years, research investigations on recognizing human activities to assess the physical and cognitive capability of humans have gained importance. This paper presents the development of a robust recognition system for Human Activity Recognition under real-world conditions. The activities considered are walking, walking upstairs (walk-up), walking downstairs (walk-dn), sitting, standing and sleeping. The proposed system consists of 3 main elements - a feature extraction from an IMU (Inertial Measurement Unit) based on the spectral and temporal analysis; a feature dimensionality reduction techniques to reduce the high dimensional feature representation, and; various model training algorithms to recognize the human activities. Different methods for feature extraction based on time and frequency domain signal properties are evaluated. The high dimensionality of extracted features results in complex model training and suffers from the curse of dimensionality. Therefore, we evaluated feature selection and transformation algorithms to improve robustness without decreasing the prediction accuracy. Our results finding shows that Random forest feature selection method, when used with Ensemble bagged classifier, provides an accuracy of 96.9% with 15 features compared to the current benchmark system that employs 561 features. We further obtained a less complex activity recognition system via Neighborhood component analysis along with Ensemble bagged classifier that yields a classification accuracy of 96.3% with only 9 features.