# Show HN: WiSense – WiFi-based human sensing in Python, no camera required > Source: > Published: 2026-09-03 08:26:07+00:00 WiFi CSI (Channel State Information) human sensing for Python -- presence, falls, breathing rate, coarse activity, and occupant count, without a camera, without a cloud API, and without PyTorch/CUDA at runtime. ``` python from wisense.core import FileCSISource, CSIBuffer, calibrate from wisense.presence import PresenceDetector with FileCSISource("capture.csv", realtime=True) as source: profile = calibrate(source, duration_seconds=30) # empty-room baseline with FileCSISource("capture.csv") as source: buffer = CSIBuffer(capacity=256) buffer.fill_from_source(source, max_frames=64) result = PresenceDetector().detect(buffer.snapshot(), calibration=profile) print(result.present, result.confidence) ``` **v0.1.0, alpha.** The statistical (non-ML) detection path for every feature module below is fully implemented, tested, and works without any model file. The optional ONNX inference upgrade path is fully implemented against `onnxruntime.InferenceSession` , but **no trained model files ship with this repository** -- see [Model Files](#model-files) below. **This has been validated with unit and integration tests against synthetic CSI data, but not yet against real ESP32 hardware** -- the maintainer doesn't currently have a device to test with. If you try it on real hardware, bug reports (especially anything in `SerialCSISource` /the ESP32-CSI-Tool line parsing, and the fixed thresholds in the statistical baselines) are genuinely the most useful thing you can contribute right now. Please open an issue with your board model, firmware version, and, if possible, a short capture file reproducing the problem. ``` pip install -e . ``` Requires Python 3.9+. Core dependencies: `numpy` , `scipy` , `onnxruntime` , `pyserial` . Install extras for development or visualization tooling: ``` pip install -e ".[dev]" # pytest, ruff, mypy, black pip install -e ".[viz]" # matplotlib ``` See [ examples/presence_demo.py](https://github.com/collabray/wisense/blob/main/examples/presence_demo.py) for a complete, runnable, **hardware-free** walkthrough (it replays a small synthetic capture bundled in `tests/fixtures/` ). Run it with: ``` python examples/presence_demo.py ``` For a real device, see [ examples/live_esp32_demo.py](https://github.com/collabray/wisense/blob/main/examples/live_esp32_demo.py), which **requires a physical ESP32** flashed with [ESP32-CSI-Tool](https://github.com/StevenMHernandez/ESP32-CSI-Tool)-compatible firmware, connected over USB serial. The full walkthrough -- connecting, calibrating, every feature module, event callbacks -- is in [ docs/usage.md](https://github.com/collabray/wisense/blob/main/docs/usage.md). API reference is in [.](https://github.com/collabray/wisense/blob/main/docs/api.md) `docs/api.md` | Module | What it does | Statistical baseline | ONNX upgrade path | |---|---|---|---| `wisense.presence` | Binary presence detection | Variance-of-amplitude thresholding against a calibration baseline | Yes | `wisense.fall` | Fall event detection with severity/confidence | Sudden-amplitude-drop-then-stillness signature | Yes | `wisense.vitals` | Passive breathing-rate estimation | FFT peak detection in the 0.15-0.5 Hz respiration band | No (statistical-only; see docstring) | `wisense.activity` | Coarse activity classification | Variance + periodicity + transient-level-shift heuristics | Yes | `wisense.people` | Occupant count estimation | Multipath/frequency-diversity clustering | No (statistical-only; see docstring) | `wisense.core` | Connection, buffering, filtering, calibration, event callbacks | -- | -- | `wisense.models` | Model download/cache/checksum/load management | -- | -- | Every detection call returns a structured `dataclass` (never a raw image or unprocessed signal) -- see `docs/api.md` for each result type's fields. ``` Capture Layer (Linux host or ESP32 device) SerialCSISource / NetworkCSISource / FileCSISource | v wisense.core CSIBuffer (ring buffer) -> calibration -> filters | v Feature modules (presence / fall / vitals / activity / people) -- statistical baseline, or ONNX Runtime inference if a model is configured | v Structured output (dataclasses) + event callbacks (on_presence_change / on_fall_detected via wisense.core.events.Monitor) ``` -- ESP32 running`SerialCSISource` [ESP32-CSI-Tool](https://github.com/StevenMHernandez/ESP32-CSI-Tool)-compatible firmware, over USB serial. This is the only capture target this repository has parsing code written and tested against.-- UDP or TCP, using a small newline-delimited JSON protocol WiSense defines itself (documented in the class docstring) -- there is no single industry-standard network CSI wire format, so bridging a different capture pipeline (e.g. a Linux host with a CSI-capable driver) to WiSense means emitting frames in this format.`NetworkCSISource` -- replays a recorded capture from disk in the WiSense CSV format (documented in the class docstring, and produced by`FileCSISource` `wisense.core.connection.write_capture_csv` ). Works fully offline, no hardware needed -- this is what the tests and`examples/presence_demo.py` use. WiSense ships **no pretrained .onnx model weights**. This is a deliberate design decision: it keeps the pip install small, and every feature module works fully without any model via its statistical baseline method (see the feature table above). The ONNX inference path (`model_path=` / `use_registry_model=` on each detector/classifier) is fully implemented against `onnxruntime.InferenceSession` , including download/cache/checksum management in `wisense.models.registry.ModelRegistry` . But **training and publishing model weights is out of scope for this repository** -- `ModelRegistry` 's default download URL (`DEFAULT_MODEL_BASE_URL` in `wisense/models/registry.py` ) is an intentional, clearly-marked placeholder that will not resolve. If you train your own model: - Point `PresenceDetector(model_path="/path/to/your/model.onnx")` (or the equivalent on`FallDetector` /`ActivityClassifier` ) directly at a local file,**or** - Host your own `.onnx` files somewhere and configure`ModelRegistry(base_url="https://your-host/...")` , then use`use_registry_model="yourmodel.onnx"` . Each detector's module docstring documents the exact input/output tensor contract your model needs to conform to (e.g. presence models must output `[P(absent), P(present)]` ). No accuracy numbers are claimed anywhere in this repository for the ONNX path, because no benchmarked model exists yet to cite one for. The statistical baseline's behavior is exercised by the test suite (see `tests/` ) but has likewise not been benchmarked against a labeled real-world dataset -- treat its outputs as a reasonable engineering default, not a validated accuracy claim, and calibrate (`wisense.core.calibrate` ) for your specific environment before relying on it. See [ROADMAP.md](https://github.com/collabray/wisense/blob/main/ROADMAP.md) for what's intentionally scoped out of this release and not yet implemented. ``` pip install -e ".[dev]" pytest ``` Every module has a `logging.getLogger("wisense.")` logger; WiSense never configures Python's root logger, so attach your own handler to see output: ``` python import logging logging.getLogger("wisense").addHandler(logging.StreamHandler()) logging.getLogger("wisense").setLevel(logging.INFO) ``` MIT -- see [LICENSE](https://github.com/collabray/wisense/blob/main/LICENSE).