Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors

Abstract

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.

Gait Monitoring and Analysis: A Mathematical Approach

“…The results contained in [ 25 ] could be useful in future developments of our research about analysis of data coming from subjects with neuromuscular disorders, as we explain in more detail in Section 5 about the physical description of our analysis. The authors of [ 26 ] developed models for reducing the complexities of extracting features from data coming, among others, from wearable sensors. The extraction of the features is based on spectral analysis, and, in turn, the frequency analysis is based on the fast Fourier transform.…”

“…The authors of [ 28 ] developed software that could detect quantitative observations that could improve the subjects’ gait. For other details about the applications of the Fourier transform to human gait activity, see also the references contained in [ 25 , 26 , 27 , 28 ]. The relevance of our results compared with the existing ones is given by identifying some indicators, primarily the entropy, which constitutes a straightforward way to quantify the deterioration of an individual’s gait from specific neuromuscular diseases.…”