Asaad Algarni1, Iqra Aijaz Abro2, Mohammed Alshehri3, Yahya AlQahtani4, Abdulmonem Alshahrani4, Hui Liu5,*
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601
- 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… More >