m monad-knowledge FIIT STU · open research vault
method

hidden-markov-model

Also known as: Hidden Markov Model, Hidden Markov model, hidden Markov model, HMM, Markov chains, Markov Chains

Description

A Hidden Markov Model is a discrete-state sequence model with a Markov transition kernel and per-state emission distributions. In CSI sensing it appears as a lightweight temporal model on top of frame-level features: activities-as-states, occupancy-bucket transitions, gait-cycle phases. Discrete-state Markov chains are also used inside cellular-automata transition probabilities for crowd modelling.

When it's used

  • Sequence-level smoothing of frame-level CSI predictions
  • Activity-of-daily-living pipelines
  • Cellular-automata transition modelling

Limitations

  • Discrete-state assumption is restrictive
  • Transition matrices need either supervision or EM training
  • Outperformed by RNNs / Transformers given enough data

Source Papers

  • chen2018_97e0 ↗ — HMM occupancy estimator
  • sreenu2019_6f76 ↗ — HMM in crowd-analysis pipeline
  • wang2015_48cf ↗ — HMM CSI activity classifier
  • guo2020_267f ↗ — HMM in fusion pipeline
Where it appears

26 vault papers use this method

Titles and DOIs only — no abstracts, no analyses.

  • WiFi Sensing with Channel State Information 2020 DOI ↗
  • Understanding and Modeling of WiFi Signal Based Human Activity Recognition 2015 DOI ↗
  • Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT 2018 DOI ↗
  • A Survey on Human Behavior Recognition Using Channel State Information 2019 DOI ↗
  • Cross-Domain WiFi Sensing with Channel State Information: A Survey 2023 DOI ↗
  • A Standard Indoor Spatial Data Model—OGC IndoorGML and Implementation Approaches 2017 DOI ↗
  • A Novel Device-Free Counting Method Based on Channel Status Information 2018 DOI ↗
  • On CSI and Passive Wi-Fi Radar for Opportunistic Physical Activity Recognition 2022 DOI ↗
  • Building occupancy estimation and detection: A review 2018 DOI ↗
  • A Survey on Fusion-Based Indoor Positioning 2020 DOI ↗
  • A Comprehensive Survey on Automatic Knowledge Graph Construction 2024 DOI ↗
  • Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning 2024 DOI ↗
  • A Survey on Green Wireless Sensing: Energy-Efficient Sensing via WiFi CSI and Lightweight Learning 2026 DOI ↗
  • Data-driven queueing modelling: a simulation case study of emergency department crowding 2026 DOI ↗
  • Crowd Counting via Wi-Fi Probe Requests: Integrating Feature Selection and Data Generation 2025 DOI ↗
  • A Comparative Study of Algorithm Usage in Library and Information Science Research: China vs. Other Countries 2024 DOI ↗
  • Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions 2024 DOI ↗
  • Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase 2024 DOI ↗
  • A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents 2023 DOI ↗
  • Data Assimilation for Agent-Based Models 2023 DOI ↗
  • Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System 2023 DOI ↗
  • Impact of occupancy prediction models on building HVAC control system performance: Application of machine learning techniques 2022 DOI ↗
  • Theory of Statistics 2020
  • Intelligent video surveillance: a review through deep learning techniques for crowd analysis 2019 DOI ↗
  • Data Science and Machine Learning: Mathematical and Statistical Methods 2019
  • Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey 2019 DOI ↗
Co-mentioned with
Support Vector Machine×14 Indoor Localization×13 Long Short-Term Memory×13 IEEE 802.11×11 Deep Learning×11 OFDM×11 Neural Network×10 Convolutional Neural Network×10 k-Nearest Neighbors×10 Principal Component Analysis×10