Metaheuristic Optimized Voting Ensemble for Recognizing Daily and Sports Activities

This research analyzes the effectiveness of several methods for categorizing human actions captured by inertial and magnetic sensor units worn on the chest, arms, and legs. Each device has tri-axial sensors, including a gyroscope, accelerometer, and magnetometer. Voting ensemble classification models, where votes are weighted and optimized with a new optimization technique, are offered as a means to actualize this classification problem. The optimization technique is a combination of the sine cosine and particle swarm optimization algorithms, and the ensemble model is made up of three classifiers: support vector machines, decision trees, and multilayer perceptron. The classifiers are checked for accuracy using three distinct cross-validation strategies. Classifiers' proper differentiation rates and computational costs are compared to help you choose the best one for your needs. When it comes to body location, sensor devices worn on the legs provide the most valuable data. From a comparison of the various sensor modalities, we can deduce that magnetometers, followed by accelerometers and gyroscopes, provide the best classification results when only a single sensor type is employed. Furthermore, the study contrasts three machine learning models—support vector machines, decision trees, and multilayer perceptron —with respect to their usability, controllability, and classifier performance. Results reveal that the suggested method performs well in categorizing both typical daily activities and athletic endeavors.