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Comprehensive Benchmark Study of Machine Learning and Deep Learning Approaches for Human Activity Recognition using the UCI HAR Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 47 |
| Year of Publication: 2025 |
| Authors: Debjyoti Ghosh, Utpal Roy |
10.5120/ijca2025925797
|
Debjyoti Ghosh, Utpal Roy . Comprehensive Benchmark Study of Machine Learning and Deep Learning Approaches for Human Activity Recognition using the UCI HAR Dataset. International Journal of Computer Applications. 187, 47 ( Oct 2025), 66-69. DOI=10.5120/ijca2025925797
Abstract
Using smartphone sensors for Human Activity Recognition (HAR) has become a crucial research field with applications in smart settings, fitness tracking, and healthcare. This work uses the widely used UCI HAR dataset to give a thorough comparative analysis of different machine learning and deep learning algorithms for HAR. Combining a deep convolutional neural network (CNN) architecture with six conventional machine learning algorithms—Random Forest, XGBoost, Support Vector Machines, k-Nearest Neighbors, and Logistic Regression— the results have been developed and assessed. To guarantee reliable performance evaluation, all models underwent a thorough evaluation process utilizing 5-fold stratified cross-validation. As our results show, the CNN architecture performed better than the others (96.2% accuracy), closely followed by the non-linear approach SVM (95.2%) and the linear method Logistic Regression (95.4%). The study provides valuable insights into the relative strengths of different algorithmic approaches for sensor-based activity recognition and offers practical guidance for selecting appropriate models for HAR applications.
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