Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data

R Deekshayini; K Kanagalakshmi

Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data

August 2025

Vol-11, Issue-4

Paper ID: 27392

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Applications

Keywords

Human Activity Recognition Daily Activity Prediction Random Forest Support Vector Machine Wearable Devices Machine Learning Feature Importance ROC-AUC Health Monitoring

Abstract

Human activity recognition with wearable sensors has vast uses in health monitoring, exercise tracking, and lifestyle management. This paper provides a comparison of the Random Forest (RF) and Support Vector Machine (SVM) models for predicting daily activity levels from the dailyActivity_merged.csv dataset. The models classify days as active or inactive based on total steps and corresponding activity measures. Performance evaluation was done based on accuracy, precision, recall, F1-score, confusion matrices, and ROC/AUC curves. Experimental results show that Random Forest classifier performs better than SVM in all the measures, giving higher predictive accuracy and generalization. Moreover, feature importance analysis shows the most crucial activity parameters that contribute most towards active day prediction. The results highlight the value of machine learning methods in accurate activity classification and their utility for guiding individualized health interventions and exercise planning.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	R Deekshayini	CMR University
2	K Kanagalakshmi	CMR University

How to Cite

Use the following formats to cite this article in your research.

APA Style

Deekshayini, R & Kanagalakshmi, K (2025). Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 3804-3812.

MLA Style

Deekshayini, R, and K Kanagalakshmi. "Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 3804-3812.

IEEE Style

R Deekshayini and K Kanagalakshmi, "Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 3804-3812, 2025.

Vancouver Style

Deekshayini R, Kanagalakshmi K. Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):3804-3812.

Harvard Style

Deekshayini, R & Kanagalakshmi, K (2025) 'Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 3804-3812.

Chicago Style

Deekshayini, R and K Kanagalakshmi. "Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3804-3812.

Turabian Style

Deekshayini, R and K Kanagalakshmi. "Comparative Study of Random Forest and SVM for Daily Activity Level Prediction Using Wearable Device Data." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3804-3812.