Assessment of ML Classifiers in Complex Human Activity Recognition Using Wearable Sensors Data
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 7
Abstract
Human Activity Recognition (HAR) is essential for understanding daily behavior patterns, and wearable sensor data serves as a reliable source for monitoring complex activities. This study uniquely evaluates the performance of nine machine learning classifiers in the context of complex human activity recognition, relying solely on wearable sensors. It offers valuable insights into classifier effectiveness for real-world applications. Data from the PAMAP2 dataset, which was collected using three wearable IMUs placed on the hand, chest, and ankle, along with a heart rate sensor, was utilized to identify six daily complex activities. A 70/30 traintest split methodology was implemented to assess classifier performance. The Random Forest (RF) classifier achieved the highest performance, boasting 93% accuracy, precision, recall, and F1-score, followed closely by the K-Nearest Neighbors (KNN) classifier, which recorded 91% across all metrics. In contrast, the Logistic Regression (LR) classifier underperformed, achieving only 55% accuracy, likely due to its limitations in handling non-linear data. These results demonstrate that RF and KNN classifiers are effective for complex human activity recognition, while linear classifiers like LR are less suitable for such tasks. Overall, the Random Forest and KNN classifiers provide reliable performance for complex human activity recognition using wearable sensors, making them excellent choices for practical applications.
Authors and Affiliations
Saira Hamid, M. Arshad Awan, Mubashar Saeed
Keywords
Related Articles
- EP ID EP761604
- DOI -
- Views 20
- Downloads 0
Event-Based Vision for Robust SLAM: An Evaluation Using Hyper E2VID Event Reconstruction Algorithm
This paper investigates the limitations of traditional visual sensors in challenging environments by integrating event-based cameras with visual SLAM (Simultaneous Localization and Mapping). The work presents a novel c...
Automated Seismic Horizon Tracking Using Advance Spectral Decomposition Method
Introduction/Importance of Study: In three-dimensional seismic interpretation, automatic horizon tracking is a critical productivity tool. However, it often fails in areas where horizons are not smooth and exhibit shar...
Optimizing UAV Wing Performance: A Computational Analysis with Computer-Based Algorithms for Composite Material Integration
Introduction/Importance of Study: The aircraft wing, a vital component, demands intricate design to balance lift generation, drag reduction, and weight minimization. In advanced UAVs (Unmanned Aerial Vehicles), priorit...
Machine Learning Prediction of Mechanical Properties in Reinforcement Bars: A Data-Driven Approach
Introduction/Importance of Study: This study addresses the pressing need for precise prediction of mechanical properties in steel reinforcement bars (rebars) through a data-driven approach utilizing machine learning te...
Developing an Arabic-Urdu Ontology of Quranic Concepts: A Semantic Approach
An Arabic-Urdu ontology system dedicated to Quranic concepts represents a necessity for protecting the semantic value and making religious texts more accessible during Quranic study. Ontology-driven annotation tools sh...