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

• URL: http://arxiv.org/abs/2311.06311v1
• Date: Thu, 9 Nov 2023 19:10:35 GMT
• Title: Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review
• Authors: Mohammad Hossein Shayesteh, Behrooz Sharokhzadeh, and Behrooz Masoumi
• Abstract summary: The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. This review paper explores the potential of game theory as a solution for HAR tasks, and suggests novel game-theoretic approaches for HAR problems.
• Score: 0.9831489366502302
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization [52.74762030521324]
  We propose a novel algorithm to learn reward functions from observed actions.<n>We provide strong theoretical guarantees for the reliability and sample efficiency of our algorithm.
  arXiv  Detail & Related papers  (2026-01-19T04:12:51Z)
• Using Agile Story Points and Game Theory Together: Better Software Planning and Development in Agile Software Development [0.0]
  This study integrates key concepts from Agile software development, Story Point estimation, and Game Theory. We propose the application of game theoretic strategies, notably the Vickrey Auction and Stag Hunt Game.
  arXiv  Detail & Related papers  (2024-08-30T18:32:14Z)
• Offline Imitation Learning Through Graph Search and Retrieval [57.57306578140857]
  Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. We propose GSR, a simple yet effective algorithm that learns from suboptimal demonstrations through Graph Search and Retrieval. GSR can achieve a 10% to 30% higher success rate and over 30% higher proficiency compared to baselines.
  arXiv  Detail & Related papers  (2024-07-22T06:12:21Z)
• DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
  We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
  arXiv  Detail & Related papers  (2024-06-10T20:08:44Z)
• Transfer Learning in Human Activity Recognition: A Survey [0.13029741239874087]
  Sensor-based human activity recognition (HAR) has been an active research area, owing to its applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has resulted in state-of-the-art performance in domains such as computer vision and natural language. We focus on these transfer learning methods in the application domains of smart home and wearables-based HAR.
  arXiv  Detail & Related papers  (2024-01-18T18:12:35Z)
• Responsible AI (RAI) Games and Ensembles [30.110052769733247]
  We provide a general framework for studying problems, which we refer to as Responsible AI (RAI) games. We provide two classes of algorithms for solving these games: (a) game-play based algorithms, and (b) greedy stagewise estimation algorithms. We empirically demonstrate the applicability and competitive performance of our techniques for solving several RAI problems, particularly around subpopulation shift.
  arXiv  Detail & Related papers  (2023-10-28T22:17:30Z)
• Overview of Human Activity Recognition Using Sensor Data [4.941233729756897]
  Human activity recognition (HAR) is used in different applications including home and workplace automation, security and surveillance as well as healthcare. We overview sensor-based HAR, discuss several important applications that rely on HAR, and highlight the most common machine learning methods that have been used for HAR. Several challenges of HAR are explored that should be addressed to further improve the robustness of HAR.
  arXiv  Detail & Related papers  (2023-09-12T10:37:42Z)
• Efficient Deep Clustering of Human Activities and How to Improve Evaluation [53.08810276824894]
  We present a new deep clustering model for human activity re-cog-ni-tion (HAR) In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models.
  arXiv  Detail & Related papers  (2022-09-17T14:12:42Z)
• Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning [65.12226891589592]
  This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming.
  arXiv  Detail & Related papers  (2022-08-15T11:08:44Z)
• Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis [4.686889458553123]
  Sensor-based human activity recognition (HAR) is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. How can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision.
  arXiv  Detail & Related papers  (2021-04-19T15:38:22Z)
• Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making [66.72253432908693]
  Kinship recognition is a challenging problem with many practical applications. We review the public resources and data challenges that enabled and inspired many to hone-in on the views. For the tenth anniversary, the demo code is provided for the various kin-based tasks.
  arXiv  Detail & Related papers  (2020-06-29T13:25:45Z)
• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities [52.59080024266596]
  We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets. We then propose a new taxonomy to structure the deep methods by challenges.
  arXiv  Detail & Related papers  (2020-01-21T09:55:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.