High-performance real-time human activity recognition using Machine Learning

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dc.contributor.authorThottempudi, Pardhu
dc.contributor.authorAcharya, Biswaranjan
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2024-11-20T16:24:42Z
dc.date.available2024-11-20T16:24:42Z
dc.date.issued2024-11-20
dc.description.abstractHuman Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. However, distinguishing between static activities, such as sitting and standing, remains a challenge, with the model showing a lower recall for sitting due to subtle postural differences. To address these limitations, we implement advanced feature extraction, data augmentation, and sensor fusion techniques, which significantly improve classification accuracy. The ease of use of the B-L475E-IOT01A kit allows for real-time activity classification, validated through the Tera Term interface, making the system ideal for practical applications in wearable devices and embedded systems. The novelty of our approach lies in the seamless integration of real-time processing capabilities with advanced machine-learning techniques, providing immediate, actionable insights. With an overall classification accuracy of 90%, this system demonstrates great potential for deployment in health monitoring, fitness tracking, and eldercare applications. Future work will focus on enhancing the system’s performance in distinguishing static activities and broadening its real-world applicability.
dc.identifier.citationThottempudi, P., Acharya, B., & Moreira, F. (2024). High-performance real-time human activity recognition using Machine Learning. Mathematics, 12(22), 3622, 1-28. https://doi.org/10.3390/math12223622. Repositório Institucional UPT. https://hdl.handle.net/11328/6007
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/11328/6007
dc.language.isoeng
dc.publisherMDPI - Multidisciplinary Digital Publishing Institute
dc.relation.hasversionhttps://doi.org/10.3390/math12223622
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHuman activity recognition
dc.subjectmachine learning
dc.subjectwearable sensors
dc.subjectreal-time classification
dc.subjectfeature extraction
dc.subjecttera term
dc.subjectsensor fusion
dc.subjecthealth monitoring
dc.subjectfitness tracking
dc.subjectdeep learning
dc.subject.fosCiências Naturais - Ciências da Computação e da Informação
dc.titleHigh-performance real-time human activity recognition using Machine Learning
dc.typejournal article
dcterms.referenceshttps://www.mdpi.com/2227-7390/12/22/3622
dspace.entity.typePublication
oaire.citation.endPage28
oaire.citation.issue22
oaire.citation.startPage1
oaire.citation.titleMathematics
oaire.citation.volume12
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e

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