A Hybrid Deep Learning Pipeline for Wearable Sensors-Based Human Activity Recognition

Abstract

Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using a novel fusion strategy combined with Bayesian-based optimization, enabling a more accurate and generalized activity recognition. The key contribution lies in the joint optimization and fusion of features via Bayesian-based subset selection, resulting in a compact and highly discriminative feature representation. These features are then fed into a Convolutional Neural Network (CNN) to effectively detect spatial-temporal patterns in activity signals. Testing on two public datasets—IM-WSHA and ENABL3S—achieved accuracy levels of 93.0% and 92.0%, respectively. The integration of advanced feature extraction methods with fusion and optimization techniques significantly enhanced detection performance, surpassing traditional methods. The obtained results establish the effectiveness of the proposed IS-DAR system for deployment in real-world activity recognition applications.