High-Performance Real-Time Human Activity Recognition Using Machine Learning

Pardhu Thottempudi, Biswaranjan Acharya () and Fernando Moreira ()
Additional contact information
Pardhu Thottempudi: Department of Electronics and Communications Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad 500090, India
Biswaranjan Acharya: Department of Computer Engineering-AI & BDA, Marwadi University, Rajkot 360003, India
Fernando Moreira: REMIT, IJP, Universidade Portucalense, 4200 Porto, Portugal

Mathematics, 2024, vol. 12, issue 22, 1-28

Abstract: 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.

Keywords: human activity recognition; machine learning; wearable sensors; real-time classification; feature extraction; tera term; sensor fusion; health monitoring; fitness tracking; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/22/3622/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/22/3622/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:22:p:3622-:d:1525010

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().