A Hidden Markov Model (HMM) is a probabilistic sequential model that represents a system as a series of unobservable latent states, where observations are generated stochastically from each hidden state and transitions between states follow Markov dynamics. In WiFi/CSI sensing research, HMMs are valuable for modeling the temporal evolution of channel state information patterns that correspond to hidden physical states such as occupancy levels, crowd counts, or human activities, enabling robust inference even when the underlying states cannot be directly measured. Key variants relevant to the field include discrete and continuous observation HMMs, as well as hierarchical HMMs that can capture multi-level state abstractions, all of which can be integrated with data assimilation frameworks or IoT-based sensing pipelines to improve the accuracy of device-free detection and crowd estimation in smart environments.
Source Papers
- A Standard Indoor Spatial Data Model—OGC IndoorGML and Implementation Approaches ↗ — A Standard Indoor Spatial Data Model—OGC IndoorGML and Imple
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- Data Assimilation for Agent-Based Models ↗ — Data Assimilation for Agent-Based Models
- Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT ↗ — Device-free occupancy detection and crowd counting in smart
- Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning ↗ — Human Activity Recognition via Wi-Fi and Inertial Sensors Wi
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol
- Physics of Human Crowds ↗ — Physics of Human Crowds
- Understanding and Modeling of WiFi Signal Based Human Activity Recognition ↗ — Understanding and Modeling of WiFi Signal Based Human Activi