[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120256-en":3,"doc-seo-120256-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120256,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","High-Performance Real-Time Human Activity Recognition Using Machine Learning","Human Activity Recognition (HAR) supports intelligent services across healthcare, fitness, and smart environments by classifying physical actions in real time. The paper proposes a machine-learning HAR system built on the B-L475E-IOT01A Discovery Kit, combining wearable accelerometer and gyroscope sensors for continuous data acquisition and processing. The model delivers strong results for dynamic activities such as walking variants, while static postures like sitting and standing remain harder, motivating advanced feature extraction, data augmentation, and sensor fusion. Experiments report 90% classification accuracy and practical real-time validation via the Tera Term interface, supporting wearable and embedded deployments. Future work targets improved static activity discrimination and wider real-world use.","mathematics  \nArticle  \nHigh-Performance Real-Time Human Activity Recognition Using Machine Learning  \nPardhu Thottempudi 1,2,3, Biswaranjan Acharya 4, * and Fernando Moreira 5,6, *  \nCitation: Thottempudi, P.; Acharya, B.; Moreira, F. High-Performance RealTime Human Activity Recognition Using Machine Learning. Mathematics 2024, 12, 3622. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/math12223622](10.3390/math12223622)  \nAcademic Editors: Qingshan Jiang and Huawen Liu  \nReceived: 30 September 2024  \nRevised: 28 October 2024  \nAccepted: 12 November 2024  \nPublished: 20 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Electronics and Communications Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad 500090, India; [pardhu.t@bvrithyderabad.edu.in or pardhu@unimap.edu.my](pardhu.t@bvrithyderabad.edu.in or pardhu@unimap.edu.my)  \n[2](2 Faculty of Electronic Engineering & Technology)[ Faculty of Electronic Engineering & Technology](2 Faculty of Electronic Engineering & Technology), [Universiti Malaysia Perlis](Universiti Malaysia Perlis), [Pauh Putra Campus](Pauh Putra Campus), Arau 02600, Perlis, Malaysia  \n3 Centre of Excellence for Micro System Technology (MiCTEC), Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Perlis, Malaysia  \n4 Department of Computer Engineering-AI & BDA, Marwadi University, Rajkot 360003, India  \n5 REMIT, IJP, Universidade Portucalense, 4200 Porto, Portugal  \n6 IEETA, Universidade de Aveiro, 3810 Aveiro, Portugal  \n* [Correspondence: biswaranjan.acharya@marwadieducation.edu.in](Correspondence: biswaranjan.acharya@marwadieducation.edu.in) (B.A.); fmoreira@upt.pt (F.M.)  \nAbstract: Human 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.  \nKeywords: human activity recognition; machine learning; wearable sensors; real-time classification; feature extraction; tera term; sensor fusion; health monitoring; fitness tracking; dee","cbCaicnZvIy1C0YT","https://ap.wps.com/l/cbCaicnZvIy1C0YT","pdf",999544,1,28,"English","en",105,"# Introduction\n## Background and applications\n## Wearable platforms and sensor streams\n## Machine-learning advances and remaining challenges\n# Method Overview\n## Hardware platform and data acquisition\n## Sensor fusion and feature extraction","[{\"question\":\"What is the main goal of the proposed HAR system?\",\"answer\":\"To classify human activities in real time using machine-learning techniques running on a low-power microcontroller platform integrated with wearable sensors.\"},{\"question\":\"Which sensors and development platform are used in the study?\",\"answer\":\"The system uses accelerometers and gyroscopes connected to the B-L475E-IOT01A Discovery Kit for efficient data acquisition and on-device processing.\"},{\"question\":\"Why is static activity recognition more difficult than dynamic activity recognition?\",\"answer\":\"Static postures such as sitting and standing have subtle postural differences that are harder for conventional sensor setups and the model to distinguish, resulting in lower recall for classes like sitting.\"}]","High-Performance Real-Time Human Activity Recognition Using Machine Learning | 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is the main goal of the proposed HAR system?","Question",{"text":75,"@type":76},"To classify human activities in real time using machine-learning techniques running on a low-power microcontroller platform integrated with wearable sensors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which sensors and development platform are used in the study?",{"text":80,"@type":76},"The system uses accelerometers and gyroscopes connected to the B-L475E-IOT01A Discovery Kit for efficient data acquisition and on-device processing.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is static activity recognition more difficult than dynamic activity recognition?",{"text":84,"@type":76},"Static postures such as sitting and standing have subtle postural differences that are harder for conventional sensor setups and the model to distinguish, resulting in lower recall for classes like 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