Human Activity Recognition: The Role of Hybrid Feature Extraction Technique in Accelerometer Data Using a Voting Classifier Approach

Human Activity Recognition has become an increasingly important research area with applications in various domains, including healthcare, sports, and smart home technologies. This study presents a comprehensive approach to human activity recognition using a combination of time-domain and frequency-domain feature extraction with advanced feature extraction techniques along with a voting classifier model. To evaluate the effectiveness of the proposed approach, a comprehensive evaluation was conducted using publicly available datasets. The results demonstrate that the proposed voting-based ensemble method with hybrid feature extraction significantly improved the accuracy of human activity recognition compared to individual classifiers and other ensemble models.