Home / Current Issue / Paper 1716485
Machine Learning for Sensor-Based Human Activity Recognition
Subject area: Science,Engineering and Technology · Area of research: Human Activty Recognition and Machine Learning
DOI: 10.64388/IREV9I10-1716485
Abstract
Human Activity Recognition (HAR) has become a key enabler in wearable health technology and fitness analytics. This paper presents a machine learning framework for classifying six physical activities—Walking, Running, Cycling, Swimming, Resting, and Yoga—using sensor-derived physiological and motion features. A dataset of 8,000 observations with 14 attributes including heart rate, steps per minute, step entropy, and distance traveled is utilized. A Random Forest classifier (n_estimators=50) is trained on an 80/20 train-test split and evaluated against a Decision Tree baseline. The Random Forest achieves a test accuracy of 85.06% with a macro-averaged F1-score of 0.83, compared to the Decision Tree's 76.5% accuracy. Feature importance analysis identifies heart rate and steps per minute as the most discriminative predictors. Correlation analysis reveals strong relationships (r ≈ 0.95) between distance and calories burned. A real-time prediction interface is implemented to demonstrate practical deployment. Results demonstrate the effectiveness of ensemble learning combined with interpretable feature analysis for robust activity recognition in resource-constrained wearable systems.
Keywords
Human Activity Recognition, Random Forest, Decision Tree, Wearable Sensors, Feature Engineering, Step Entropy, Machine Learning, Fitness Analytics
References
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How to cite this paper
@article{1716485,
author = {Chandan Mukherjee, Prince Kumar Singh, Prof. (Dr) Sanjay Pachauri, Dr. Ishrat Ali},
title = {Machine Learning for Sensor-Based Human Activity Recognition},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1972-1976},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716485.pdf},
abstract = {Human Activity Recognition (HAR) has become a key enabler in wearable health technology and fitness analytics. This paper presents a machine learning framework for classifying six physical activities—Walking, Running, Cycling, Swimming, Resting, and Yoga—using sensor-derived physiological and motion features. A dataset of 8,000 observations with 14 attributes including heart rate, steps per minute, step entropy, and distance traveled is utilized. A Random Forest classifier (n_estimators=50) is trained on an 80/20 train-test split and evaluated against a Decision Tree baseline. The Random Forest achieves a test accuracy of 85.06% with a macro-averaged F1-score of 0.83, compared to the Decision Tree's 76.5% accuracy. Feature importance analysis identifies heart rate and steps per minute as the most discriminative predictors. Correlation analysis reveals strong relationships (r ≈ 0.95) between distance and calories burned. A real-time prediction interface is implemented to demonstrate practical deployment. Results demonstrate the effectiveness of ensemble learning combined with interpretable feature analysis for robust activity recognition in resource-constrained wearable systems.},
keywords = {Human Activity Recognition, Random Forest, Decision Tree, Wearable Sensors, Feature Engineering, Step Entropy, Machine Learning, Fitness Analytics},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716485}
}