Show HN: WiSense – WiFi-based human sensing in Python, no camera required

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.

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 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 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, which

requires a physical ESP32 flashed with

ESP32-CSI-Tool-compatible firmware, connected over USB serial.

The full walkthrough -- connecting, calibrating, every feature module, event callbacks -- is in docs/usage.md. API reference is in

.

docs/api.md

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 runningSerialCSISource

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 byFileCSISource

wisense.core.connection.write_capture_csv

). Works fully offline, no hardware needed -- this is what the tests andexamples/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 onFallDetector

/ActivityClassifier

) directly at a local file,or - Host your own .onnx

files somewhere and configureModelRegistry(base_url="https://your-host/...")

, then useuse_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 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.<module>")

logger; WiSense never configures Python's root logger, so attach your own handler to see output:

import logging
logging.getLogger("wisense").addHandler(logging.StreamHandler())
logging.getLogger("wisense").setLevel(logging.INFO)

MIT -- see LICENSE.