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

arXiv: https://arxiv.org/abs/2311.06311 · HTML: https://mlsanity.com/paper/2311.06311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 119 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [Ensembles of Deep LSTM Learners for Activity Recognition using Wearables](https://mlsanity.com/paper/1703.09370/) (2017)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Attention Flows are Shapley Value Explanations](https://mlsanity.com/paper/2105.14652/) (2021)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Deep Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1807.07963/) (2018)
- [Deep Activity Recognition Models with Triaxial Accelerometers](https://mlsanity.com/paper/1511.04664/) (2015)
- [Personalized Human Activity Recognition Using Convolutional Neural Networks](https://mlsanity.com/paper/1801.08252/) (2018)
- [Online Boosting Algorithms for Multi-label Ranking](https://mlsanity.com/paper/1710.08079/) (2017)
- [Concurrent Activity Recognition with Multimodal CNN-LSTM Structure](https://mlsanity.com/paper/1702.01638/) (2017)
- [Multi-modality Sensor Data Classification with Selective Attention](https://mlsanity.com/paper/1804.05493/) (2018)
- [Personalizing human activity recognition models using incremental learning](https://mlsanity.com/paper/1905.12628/) (2019)
- [Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling](https://mlsanity.com/paper/1805.07233/) (2018)
- [Multi-agent Attentional Activity Recognition](https://mlsanity.com/paper/1905.08948/) (2019)
- [SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring](https://mlsanity.com/paper/1810.03986/) (2018)
- [A game-theoretic framework for classifier ensembles using weighted majority voting with local accuracy estimates](https://mlsanity.com/paper/1302.0540/) (2013)
- [Game Theory for Signal Processing in Networks](https://mlsanity.com/paper/1506.00982/) (2015)

## Abstract (arXiv)

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.