game theory solutions in sensor-based human activity recognition: a review

The human activity recognition (har) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. despite significant advances in har, critical challenges still exist. game theory has emerged as a promising solution to address these challenges in machine learning problems including har. however, there is a lack of research work on applying game theory solutions to the har problems. this review paper explores the potential of game theory as a solution for har tasks, and bridges the gap between game theory and har research work by suggesting novel game-theoretic approaches for har problems. the contributions of this work include exploring how game theory can improve the accuracy and robustness of har models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing har methods. the objective is to provide insights into the potential of game theory as a solution for sensor-based har, and contribute to develop a more accurate and efficient recognition system in the future research directions.