Machine Learning-Based Multi-Class Classification of Human Fitness Activities for Personalized Wellness Solutions

Authors

  • Pochampally Sravanthi, Mahmadh Sanya Mirza, Kommera Srija, Akkapally Dheeraj, Enjapuri Raj Kumar

Keywords:

Keywords: Human activity recognition, Machine Learning, Shimmer Sensors, Wearable Devices, Health Monitoring, Activity Classification.

Abstract

Human activity classification plays a vital role in health monitoring systems, enabling the accurateidentification and analysis of physical activities through wearable sensor data. This study focuses ondeveloping a robust machine learning framework for human activity classification using Shimmerwearable sensors. The existing system employs Gradient Boosting (GB) Classifier, providing a baselinefor evaluating classification accuracy.

References

J. Hayano, H. Yamamoto, I. Nonaka, M. Komazawa, K. Itao, N. Ueda, H. Tanaka, E. Yuda. Quantitative detection of sleep apnea with wearable watch device . PLoS ONE , 15 (2020), e0237279. [10.1371/journal.pone.0237279](https://doi.org/10.1371/journal.pone.0237279).

F. Delmastro, F.D. Martino, C. Dolciotti. Cognitive training and stress detection in MCI frail older people through wearable sensors and machine learning . IEEE Access , 8 (2020), pp. 65573.

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Published

2025-04-08

How to Cite

Pochampally Sravanthi, Mahmadh Sanya Mirza, Kommera Srija, Akkapally Dheeraj, Enjapuri Raj Kumar. (2025). Machine Learning-Based Multi-Class Classification of Human Fitness Activities for Personalized Wellness Solutions. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 164–177. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2285

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