Human Motion Pattern Recognition Based on the CNN-BiLSTM-Attention Algorithm.
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| Title: | Human Motion Pattern Recognition Based on the CNN-BiLSTM-Attention Algorithm. |
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| Authors: | Yu, Lie1 lyu@wtu.edu.cn, Chen, Sijun2 3173578669@qq.com |
| Source: | IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2509-2523. 15p. |
| Subjects: | Human activity recognition, Machine learning, Rehabilitation, Medical informatics, Detectors, Sports biomechanics |
| Abstract: | With increasing healthcare demands, motion pattern recognition analysis has become increasingly critical. This study focuses on level walking, upstairs, downstairs, upslope, and downslope, innovatively proposing a lightweight inertial measurement unit (IMU) sensor motion pattern recognition method based on the convolutional neural network-bidirectional long short-term memory-attention (CNN-BiLSTM-Attention) algorithm. In practical application, ten subjects wore devices equipped with two IMU modules each on the mid-thigh region. These modules precisely captured key motion data--including acceleration and angular velocity--during human movement (3000 sets). Multi-angle discrimination analysis and goodness-of-fit analysis were conducted on the raw data, with the acceleration data along the Y-axis ultimately being selected as the classification source data. Twelve-dimensional feature data were extracted from the experimental data, which were then partitioned into a training set and a test set in a 7:3 ratio. Following multiple comparative experiments, results demonstrate that the CNN-BiLSTM-Attention algorithm outperforms other optimised algorithms. Maximum values of 100% precision, 100% recall, and 99.72% F1 score were achieved across these three metrics, with a peak classification accuracy of 99.44%. With its convenient wearability, reliable data acquisition, and high accuracy, this system provides robust technical support and data assurance for multiple fields, including sports biomechanics research, rehabilitation therapy assessment, and sports training optimisation. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195026883 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Human Motion Pattern Recognition Based on the CNN-BiLSTM-Attention Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Lie%22">Yu, Lie</searchLink><relatesTo>1</relatesTo><i> lyu@wtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Sijun%22">Chen, Sijun</searchLink><relatesTo>2</relatesTo><i> 3173578669@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jul2026, Vol. 56 Issue 7, p2509-2523. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Human+activity+recognition%22">Human activity recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Rehabilitation%22">Rehabilitation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+informatics%22">Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Sports+biomechanics%22">Sports biomechanics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With increasing healthcare demands, motion pattern recognition analysis has become increasingly critical. This study focuses on level walking, upstairs, downstairs, upslope, and downslope, innovatively proposing a lightweight inertial measurement unit (IMU) sensor motion pattern recognition method based on the convolutional neural network-bidirectional long short-term memory-attention (CNN-BiLSTM-Attention) algorithm. In practical application, ten subjects wore devices equipped with two IMU modules each on the mid-thigh region. These modules precisely captured key motion data--including acceleration and angular velocity--during human movement (3000 sets). Multi-angle discrimination analysis and goodness-of-fit analysis were conducted on the raw data, with the acceleration data along the Y-axis ultimately being selected as the classification source data. Twelve-dimensional feature data were extracted from the experimental data, which were then partitioned into a training set and a test set in a 7:3 ratio. Following multiple comparative experiments, results demonstrate that the CNN-BiLSTM-Attention algorithm outperforms other optimised algorithms. Maximum values of 100% precision, 100% recall, and 99.72% F1 score were achieved across these three metrics, with a peak classification accuracy of 99.44%. With its convenient wearability, reliable data acquisition, and high accuracy, this system provides robust technical support and data assurance for multiple fields, including sports biomechanics research, rehabilitation therapy assessment, and sports training optimisation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 2509 Subjects: – SubjectFull: Human activity recognition Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Rehabilitation Type: general – SubjectFull: Medical informatics Type: general – SubjectFull: Detectors Type: general – SubjectFull: Sports biomechanics Type: general Titles: – TitleFull: Human Motion Pattern Recognition Based on the CNN-BiLSTM-Attention Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Lie – PersonEntity: Name: NameFull: Chen, Sijun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 56 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
| ResultId | 1 |
