Performance Evaluation of Wrist Pulse Signals by using Hybrid Artificial Bee Colony with Feed Forward Neural Networks.
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| Title: | Performance Evaluation of Wrist Pulse Signals by using Hybrid Artificial Bee Colony with Feed Forward Neural Networks. |
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| Authors: | VIJAYKUMAR, V. R.1, VIJAYAGOPAL, K.2 vijayagopal_k@rediffmail.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2025, Vol. 32 Issue 5, p1624-1630. 7p. |
| Subjects: | Feedforward neural networks, Heart rate monitoring, Signal processing, Internet of things, Bees algorithm, Patient monitoring |
| Abstract: | People are increasingly suffering from various health issues, with a noticeable rise in frequency. This has created a substantial global demand for health assessments, as these problems are affecting all socioeconomic groups more widely. Technological advancements in disease prevention and health maintenance have led to the development of new fields, such as monitoring systems. Heart rate, which is the average number of times the heart beats per minute, reflects various physical states, including workload, stress, attention levels, drowsiness, and the activity of the nervous system. Deployment of IoT based pulse sensor devices is a more effective way of detecting pulse signal from wrist that is termed wrist pulse signal. Using the SN11574 model pulse sensor, real-time signals from both healthy and unhealthy individuals can be collected with the help of an Arduino controller. The captured signals are processed such as noise removal, signal normalization and signal quantization etc. The sensor's analog pulse rate readings are converted into digital data, which is then analyzed using the Fast Fourier Transform (FFT). This method has an advantage over traditional FFT approaches in reducing the influence of breathing patterns and avoiding the mixing of breathing and heartbeat signals. The classification of pulse signals for healthy and unhealthy individuals is done using a combination of the Hybrid Artificial Bee Colony and a Feed Forward Neural Network (HABCFFNN) to predict the individual's health status. The pulse databases are implemented in this HABCFFNN for performance evaluation. The wrist pulse signal databases, POLYU and Wojcikowski, are used in this method. The output of the neural network is fed into an analysis report, where some statistical notations are considered. Performance measurements such as accuracy, precision, sensitivity, specificity, etc., are used to highlight the effectiveness of this method compared to other related work. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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: 189659636 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance Evaluation of Wrist Pulse Signals by using Hybrid Artificial Bee Colony with Feed Forward Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22VIJAYKUMAR%2C+V%2E+R%2E%22">VIJAYKUMAR, V. R.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22VIJAYAGOPAL%2C+K%2E%22">VIJAYAGOPAL, K.</searchLink><relatesTo>2</relatesTo><i> vijayagopal_k@rediffmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2025, Vol. 32 Issue 5, p1624-1630. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+rate+monitoring%22">Heart rate monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+monitoring%22">Patient monitoring</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: People are increasingly suffering from various health issues, with a noticeable rise in frequency. This has created a substantial global demand for health assessments, as these problems are affecting all socioeconomic groups more widely. Technological advancements in disease prevention and health maintenance have led to the development of new fields, such as monitoring systems. Heart rate, which is the average number of times the heart beats per minute, reflects various physical states, including workload, stress, attention levels, drowsiness, and the activity of the nervous system. Deployment of IoT based pulse sensor devices is a more effective way of detecting pulse signal from wrist that is termed wrist pulse signal. Using the SN11574 model pulse sensor, real-time signals from both healthy and unhealthy individuals can be collected with the help of an Arduino controller. The captured signals are processed such as noise removal, signal normalization and signal quantization etc. The sensor's analog pulse rate readings are converted into digital data, which is then analyzed using the Fast Fourier Transform (FFT). This method has an advantage over traditional FFT approaches in reducing the influence of breathing patterns and avoiding the mixing of breathing and heartbeat signals. The classification of pulse signals for healthy and unhealthy individuals is done using a combination of the Hybrid Artificial Bee Colony and a Feed Forward Neural Network (HABCFFNN) to predict the individual's health status. The pulse databases are implemented in this HABCFFNN for performance evaluation. The wrist pulse signal databases, POLYU and Wojcikowski, are used in this method. The output of the neural network is fed into an analysis report, where some statistical notations are considered. Performance measurements such as accuracy, precision, sensitivity, specificity, etc., are used to highlight the effectiveness of this method compared to other related work. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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: Identifiers: – Type: doi Value: 10.17559/TV-20240828001950 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 1624 Subjects: – SubjectFull: Feedforward neural networks Type: general – SubjectFull: Heart rate monitoring Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Bees algorithm Type: general – SubjectFull: Patient monitoring Type: general Titles: – TitleFull: Performance Evaluation of Wrist Pulse Signals by using Hybrid Artificial Bee Colony with Feed Forward Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: VIJAYKUMAR, V. R. – PersonEntity: Name: NameFull: VIJAYAGOPAL, K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 32 – Type: issue Value: 5 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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