In recent years, human physical activity recognition has increasingly attracted attention from different research
fields such as healthcare, computer-human interaction, lifestyle monitoring, and athletics. Deep learning models
have been extensively employed in developing physical activity recognition systems. To improve these models,
their hyperparameters need to be initialized with optimal values. However, tuning these hyperparameters
manually is time-consuming and may lead to inaccurate results. Moreover, the application of these models to
different data resources and the integration of their results into the overall data processing pipeline are challenging
issues in physical activity recognition systems. In this paper, we propose a novel ensemble method for
physical activity recognition based on a deep transformer-based time-series classification model that uses heart
rate, speed, and distance time-series data to recognize physical activities. In particular, we develop a modified
arithmetic optimization algorithm to automatically adjust the optimal values of the classification models’
hyperparameters. Moreover, a reinforcement learning-based ensemble approach is proposed to optimally integrate
the results of the classification models obtained using heart rate, speed, and distance time-series data and,
subsequently, recognize the physical activities. Experiments performed on a real-world dataset demonstrated
that the proposed method achieves promising efficiency in comparison to other state-of-the-art models. More
specifically, the proposed method increases the performance compared to the second-best performer by around
3.44 %, 9.45 %, 5.43 %, 2.54 %, and 7.53 % based on accuracy, precision, recall, specificity, and F1-score
evaluation metrics, respectively.
