Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture specifically designed to learn temporal dependencies in sequential data by using gating mechanisms (input, forget, and output gates) to selectively retain or discard information over time. In WiFi CSI sensing, LSTMs are valuable because CSI signals are inherently time-series data whose temporal dynamics encode meaningful patterns related to human presence, movement, and occupancy, making sequential modeling essential for accurate classification. A common and effective variant in this domain is the CNN+LSTM hybrid model, which combines convolutional layers for spatial or spectral feature extraction from raw CSI data with LSTM layers for capturing the temporal evolution of those features, as seen in people counting and occupancy estimation tasks.
Source Papers
- A Framework to Estimate Classroom Occupancy using WiFi Channel State Information ↗ — A Framework to Estimate Classroom Occupancy using WiFi Chann
- A Survey on Green Wireless Sensing: Energy-Efficient Sensing via WiFi CSI and Lightweight Learning ↗ — A Survey on Green Wireless Sensing: Energy-Efficient Sensing
- A Survey on Human Behavior Recognition Using Channel State Information ↗ — A Survey on Human Behavior Recognition Using Channel State I
- A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning ↗ — A low-cost automatic people-counting system at bus stops usi
- A survey of recent advances in CNN-based single image crowd counting and density estimation ↗ — A survey of recent advances in CNN-based single image crowd
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection ↗ — BLE Can See: A Reinforcement Learning Approach for RF-based
- CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework for Queue Counting ↗ — CRPF-QC: An Efficient CSI Recurrence Plot-Based Framework fo
- CSI-Based People Counting in WiFi Networks: Leveraging Occupancy Detection ↗ — CSI-Based People Counting in WiFi Networks: Leveraging Occup
- CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sensing ↗ — CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sens
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing ↗ — Context-Aware Predictive Coding: A Representation Learning F
- Data Assimilation for Agent-Based Models ↗ — Data Assimilation for Agent-Based Models
- Data-driven Crowd Modeling Techniques: A Survey ↗ — Data-driven Crowd Modeling Techniques: A Survey
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Efficient machine learning for Wi-Fi CSI-based human activity recognition using fast Monte Carlo based feature extraction ↗ — Efficient machine learning for Wi-Fi CSI-based human activit
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol
- Recent trends in crowd analysis: A review ↗ — Recent trends in crowd analysis: A review
- SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing ↗ — SenseFi: A library and benchmark on deep-learning-empowered
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information
- WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities ↗ — WiFi-Based Human Sensing With Deep Learning: Recent Advances
- WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing ↗ — WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activi