RT Journal Article
T1 Analysis and recognition of human gait activity based on multimodal sensors
A1 Teran-Pineda, Diego
A1 Thurnhofer-Hemsi, Karl
A1 Domínguez-Merino, Enrique
K1 Marcha atlética
K1 Fisiología humana
K1 Sensores biomédicos
AB Remote health monitoring plays a significant role in research areas related to medicine, neurology, rehabilitation, and robotic systems. These applications include Human Activity Recognition (HAR) using wearable sensors, signal processing, mathematical methods, and machine learning to improve the accuracy of remote health monitoring systems. To improve the detection and accuracy of human activity recognition, we create a novel method to reduce the complexities of extracting features using the HuGaDB dataset. Our model extracts power spectra; due to the high dimensionality of features, sliding windows techniques are used to determine frequency bandwidth automatically, where an improved QRS algorithm selects the first dominant spectrum amplitude. In addition, the bandwidth algorithm has been used to reduce the dimensionality of data, remove redundant dimensions, and improve feature extraction. In this work, we have considered widely used machine learning classifiers. Our proposed method was evaluated using the accelerometer angles spectrum installed in six parts of the body and then reducing the bandwidth to know the evolution. Our approach attains an accuracy rate of 95.1% in the HuGaDB dataset with 70% of bandwidth, outperforming others in the human activity recognition accuracy.
PB MDPI
YR 2023
FD 2023-03-22
LK https://hdl.handle.net/10630/26961
UL https://hdl.handle.net/10630/26961
LA eng
NO Teran-Pineda D, Thurnhofer-Hemsi K, Dominguez E. Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors. Mathematics. 2023; 11(6):1538. https://doi.org/10.3390/math11061538
NO Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026