Design and Evaluation of a Multi-Metric Machine Learning Model for Human Activities Reorganization with Emphasis on Specificity and Real Time Adaptability.
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| Title: | Design and Evaluation of a Multi-Metric Machine Learning Model for Human Activities Reorganization with Emphasis on Specificity and Real Time Adaptability. |
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| Authors: | Sharma, Pradeep Kumar1, Mathur, Harsh2 |
| Source: | Educational Administration: Theory & Practice. 2024, Vol. 30 Issue 11, p1755-1765. 11p. |
| Abstract: | Human activity recognition (HAR) has emerged as a crucial area of research due to its widespread applications in various domains, including healthcare, smart environments, and assistive technologies. With the proliferation of wearable sensors and the Internet of Things (IoT), the ability to accurately sense and interpret human activities has become increasingly important. Machine learning models have played a pivotal role in advancing HAR systems, enabling the effective recognition of complex activities from sensor data. This research paper provides a comprehensive review of machine learning models employed for human activity recognition, encompassing both traditional techniques and state-of-the-art deep learning approaches as well as a proposed approach. It discusses the challenges and considerations involved in activity recognition, such as data acquisition, feature extraction, and model selection. Additionally, the paper presents a comparative analysis of various machine learning models, evaluating their performance, strengths, and limitations across different activity recognition tasks and datasets and comparison among different machine learning models and proposed model. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Administration: Theory & Practice is the property of Educational Administration: Theory & Practice 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: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 192328003 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Design and Evaluation of a Multi-Metric Machine Learning Model for Human Activities Reorganization with Emphasis on Specificity and Real Time Adaptability. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharma%2C+Pradeep+Kumar%22">Sharma, Pradeep Kumar</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mathur%2C+Harsh%22">Mathur, Harsh</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Administration%3A+Theory+%26+Practice%22">Educational Administration: Theory & Practice</searchLink>. 2024, Vol. 30 Issue 11, p1755-1765. 11p. – Name: Abstract Label: Abstract Group: Ab Data: Human activity recognition (HAR) has emerged as a crucial area of research due to its widespread applications in various domains, including healthcare, smart environments, and assistive technologies. With the proliferation of wearable sensors and the Internet of Things (IoT), the ability to accurately sense and interpret human activities has become increasingly important. Machine learning models have played a pivotal role in advancing HAR systems, enabling the effective recognition of complex activities from sensor data. This research paper provides a comprehensive review of machine learning models employed for human activity recognition, encompassing both traditional techniques and state-of-the-art deep learning approaches as well as a proposed approach. It discusses the challenges and considerations involved in activity recognition, such as data acquisition, feature extraction, and model selection. Additionally, the paper presents a comparative analysis of various machine learning models, evaluating their performance, strengths, and limitations across different activity recognition tasks and datasets and comparison among different machine learning models and proposed model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Administration: Theory & Practice is the property of Educational Administration: Theory & Practice 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=192328003 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53555/kuey.v30i11.9960 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1755 Titles: – TitleFull: Design and Evaluation of a Multi-Metric Machine Learning Model for Human Activities Reorganization with Emphasis on Specificity and Real Time Adaptability. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharma, Pradeep Kumar – PersonEntity: Name: NameFull: Mathur, Harsh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13004832 Numbering: – Type: volume Value: 30 – Type: issue Value: 11 Titles: – TitleFull: Educational Administration: Theory & Practice Type: main |
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