Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JADM-11-2_009
تاریخ نمایه سازی: 27 تیر 1402
Abstract:
This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, and computer science. In recent years, game theory has been increasingly applied to machine learning challenges, including HAR, as a potential solution to improve recognition performance and efficiency of recognition algorithms. The review covers the shared challenges between HAR and machine learning, compares previous work on traditional approaches to HAR, and discusses the potential advantages of using game theory. It discusses different game theory approaches, including non-cooperative and cooperative games, and provides insights into how they can improve the HAR systems. The authors propose new game theory-based approaches and evaluate their effectiveness compared to traditional approaches. Overall, this review paper contributes to expanding the scope of research in HAR by introducing game-theoretic concepts and solutions to the field and provides valuable insights for researchers interested in applying game-theoretic approaches to HAR.
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Authors
Behrooz Shahrokhzadeh
Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Mohammad Hossein Shayesteh
Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Behrooz Masoumi
Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
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