The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data

Human Activity Recognition (HAR) plays a crucial role in numerous applications, ranging from healthcare to sports analytics. This study presents a novel approach to HAR that combines Artificial Neural Networks (ANNs) and Random Forests to enhance the accuracy of HAR in diverse real-world conditions, especially when dealing with noisy and imperfect data. ANNs are known for extracting intricate features from raw data, making them well-suited for classification. Random Forest excels at learning from multiple decision trees and utilizing collective knowledge to make predictions, making it suitable for handling data in real-world applications. Harnessing the power of ANNs in feature extraction, coupled with the collective decision-making capability of Random Forest, the combined model demonstrates improved accuracy in classifying human activities. This study showcases the potential of combining ANN and Random Forest in classifying multi-dimensional Inertial Measurement Unit (IMU) data, a widely-used data source in HAR. By leveraging the strengths of both ANN and Random Forest, the combined model addresses the challenges associated with real, imperfect data, leading to a more robust and accurate classification model. The results highlight the effectiveness of feature extraction by ANNs and underscore the importance of incorporating Random Forest in HAR systems to obtain 98.84% accuracy. The findings of this study offer valuable insights into the synergistic effects of combining ANN and Random Forest for HAR. The outcomes can contribute to developing more reliable and effective HAR systems, with potential applications in healthcare monitoring, activity recognition in smart environments, and other domains requiring accurate human activity classification.