Open Access

ARTICLE

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

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

1 Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia
2 Faculty of Computing and AI, Air University, Islamabad, 44000, Pakistan
3 Department of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
4 Department of Informatics and Computer Systems, King Khalid University, Abha, 61421, Saudi Arabia
5 Cognitive Systems Lab, University of Bremen, Bremen, 28359, Germany

* Corresponding Author: Hui Liu. Email:

(This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)

Computers, Materials & Continua 2025, 84(3), 5879-5896. https://doi.org/10.32604/cmc.2025.064601

Received 19 February 2025; Accepted 26 May 2025; Issue published 30 July 2025

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.

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Keywords

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