Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

TL;DR

This review explores how game theory can enhance sensor-based human activity recognition by improving accuracy and robustness, proposing novel approaches to address existing challenges in HAR systems.

Contribution

It bridges the gap between game theory and HAR research, suggesting new game-theoretic methods to improve recognition accuracy and robustness.

Findings

Game theory can enhance HAR model accuracy.

Game-theoretic concepts can optimize recognition algorithms.

Discussion of game-theoretic approaches versus existing HAR methods.

Abstract

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.