Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors
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 ac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/6/1538 |
| _version_ | 1797610277468897280 |
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| author | Diego Teran-Pineda Karl Thurnhofer-Hemsi Enrique Dominguez |
| author_facet | Diego Teran-Pineda Karl Thurnhofer-Hemsi Enrique Dominguez |
| author_sort | Diego Teran-Pineda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:13:09Z |
| format | Article |
| id | doaj.art-35d0a01294b341c991af92541494f1df |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:09Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-35d0a01294b341c991af92541494f1df2023-11-17T12:30:01ZengMDPI AGMathematics2227-73902023-03-01116153810.3390/math11061538Analysis and Recognition of Human Gait Activity Based on Multimodal SensorsDiego Teran-Pineda0Karl Thurnhofer-Hemsi1Enrique Dominguez2Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, SpainDepartment of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, SpainDepartment of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071 Málaga, SpainRemote 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.https://www.mdpi.com/2227-7390/11/6/1538multimodal sensormotion classificationcomputational intelligencecomplex feature extractionactivity recognitionQRS algorithm |
| spellingShingle | Diego Teran-Pineda Karl Thurnhofer-Hemsi Enrique Dominguez Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors Mathematics multimodal sensor motion classification computational intelligence complex feature extraction activity recognition QRS algorithm |
| title | Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors |
| title_full | Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors |
| title_fullStr | Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors |
| title_full_unstemmed | Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors |
| title_short | Analysis and Recognition of Human Gait Activity Based on Multimodal Sensors |
| title_sort | analysis and recognition of human gait activity based on multimodal sensors |
| topic | multimodal sensor motion classification computational intelligence complex feature extraction activity recognition QRS algorithm |
| url | https://www.mdpi.com/2227-7390/11/6/1538 |
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