> ## Documentation Index > Fetch the complete documentation index at: https://docs.waveyhq.dev/llms.txt > Use this file to discover all available pages before exploring further. # Motion and Activity Detection Using WiFi CSI > Detect motion via variance thresholds and classify human activities through three generations of CSI-based HAR, from physics-linked models to dual-stream transformers. Movement is the strongest signal in WiFi CSI. Limb acceleration creates broadband energy in the 1–5 Hz band, variance spikes across subcarriers, and channel changes within a single packet window. Wavey detects that something moved with a simple threshold, and supports coarser activity classification where labeled data from the deployment environment justifies a classifier. ## From motion to activity **Motion detection** is a threshold on variance or spectral power — binary, fast, and environment-agnostic within a single deployment. **Human activity recognition (HAR)** has three generations in the published literature. Each generation trades model complexity against cross-environment robustness. Before deep learning dominated, CARM built quantitative models: * **CSI–speed** — amplitude change frequency maps to limb speed via Doppler (path-length change of one wavelength = 2π phase shift). * **CSI–activity** — activities differ by which body parts move at which speeds (legs periodic in walking, torso impulse in falling). Features: PCA denoising, discrete wavelet transform, HMM per activity for state transitions. Greater than 96% on COTS WiFi using **amplitude**, not phase — CFO on commodity hardware makes raw phase unusable for macro-motion HAR. Still a strong baseline because features encode mechanism, not just correlation. STFT tiles (time × frequency) fed to CNNs. Learns motion textures without explicit speed models. Works when you have labels; brittle cross-room without adaptation. CSI has two informative axes: | Stream | What it captures | Missed by LSTM-only | | ----------------- | ------------------------------------- | ------------------- | | Channel-over-time | How each subcarrier evolves | — | | Time-over-channel | Subcarrier correlation at one instant | Yes | THAT's Multi-scale Convolution Augmented Transformer (MCAT) processes both streams with Gaussian range encoding. +2.2 pts accuracy over ABLSTM, 1.8–3.4× faster — because a single temporal sample is too elementary to represent a CSI pattern. ## The cross-room transfer problem CrossSense (MobiCom 2018) showed gesture recognition accuracy collapse from greater than 90% in-room to approximately 20% cross-site with a single global model. Their fix: a **roaming model** that synthesizes target-environment training data from a small calibration set, plus a **mixture-of-experts** runtime selector (DTW-based) that routes signals to specialized models. Separately, the EI framework (MobiCom 2018) uses adversarial training — feature extractor vs domain discriminator — to strip environment-specific information before classification. Without one of these (or per-site fine-tuning), do not deploy a model trained elsewhere. See the [sensing pipeline](/concepts/sensing-pipeline) for the full adaptation landscape. ## What Wavey targets Wavey emits motion events (intensity, duration) as the primary output. Coarse activity classes are supported where labeled data from the deployment environment justifies a classifier. Multi-person 3D pose (CVPR 2024: approximately 92–125 mm joint error with dense WiFi nodes in a 4×3.5 m rig) is research territory — not ESP32 territory. See the [detection ladder](/concepts/detection-ladder), rung 4. For building a classifier from scratch, read [building an activity classifier on CSI](/posts/wifi-motion-detection-without-camera). ## Further Reading HAR feasibility and limits Motion as a zone alarm Building an activity classifier from scratch Set up your first Wavey deployment