An open-source, field-deployable sensor pipeline framework for animal-augmented environmental and biological sensing. Hardware reference designs, firmware, and ML models — fork it, deploy it, extend it.
We mapped the landscape before writing a line of code. Three buckets — closed product companies, satellite tracking, lab-only behavior tools — and a gap in the middle that nobody is building.
Dognosis (patented dog-BCI cancer detection), SpotitEarly (trained-dog DTC screening), Owlstone (synthetic e-nose) — all closed, all vertical. None expose a substrate layer the rest of the field can build on.
ICARUS at Max Planck tracks 100,000+ animals via satellite with GPS + accelerometer tags. Useful, but single-modality and altitude-limited. The ground-level multi-modal pipeline — chemical, acoustic, seismic, behavioral — does not exist.
DeepLabCut, AlphaTracker, MARS, LabGym — all computer-vision pose estimation, all built for inside-the-lab neuroscience. None are field-deployable. None frame the animal as a sensor for external phenomena.
AnimalSensePipe is software and reference hardware that sits between raw multi-modal animal and environmental sensor data and a trained ML model. EmbedIQ on the device, Python on the host. Every layer is pluggable. Bring your own sensors, your own animals, your own model.
EmbedIQ-based sensor drivers, hardware time-sync across modalities, on-device calibration, ring-buffer storage, MQTT/HTTP egress with backpressure.
Streams from animal and site packs land in a time-series database (InfluxDB or TimescaleDB), tagged by deployment session and animal ID.
Python SDK with per-animal baseline learning, per-site environmental baseline, time-alignment, and a multi-modal dataframe builder.
Plug-in model interface with one reference model — anomaly detection on multi-modal time series. Swap in your own model without touching the pipeline.
$ animalsensepipe init --kit collar-v0 → firmware flashed · time-sync verified (animal_pack, site_pack) $ animalsensepipe deploy --location home --animal max → streaming · imu · audio · seismic · env (session_20260511.aspipe)
Every part is commodity, every supply is known, every line of firmware is open. No custom silicon, no exotic sensors. The hardware story is "EmbedIQ plus commodity parts" — exactly the showcase the community can fork.
Whether you build PCBs, write firmware, train models, run a deployment, or want to help without writing code yet — there's a clear entry point and a "good first issue" tag for each.
You like PCBs, 3D printing, sensor selection. Help refine the BOM, prototype the enclosure, test off-the-shelf alternatives, document build photos.
good-first-issue · hardwareYou work in embedded C / Rust. Help build sensor drivers on EmbedIQ, hardware time-sync, ring-buffer storage, MQTT egress. The EmbedIQ docs are public.
good-first-issue · firmwareYou're an ML student or researcher. Improve the anomaly-detection reference model, add new fusion primitives, contribute notebooks for new use cases.
good-first-issue · mlYou have access to a pet, farm, or field site. Deploy a kit, collect data, publish your dataset. Your real-world deployment is the most valuable contribution.
good-first-issue · fieldYou want to help but aren't writing code yet. Write build tutorials, translate the docs, improve the website copy, run the GitHub Discussions community. This is the fastest way to make the project usable for people in other languages and disciplines.
good-first-issue · docsHonest about scope. Honest about what's funded. Honest about what's exploratory. No dates — direction.
This part is up-front, not buried at the bottom. The science around animal-based prediction is genuinely contested. We don't make claims the science doesn't support.
AnimalSensePipe is the open infrastructure that lets researchers, conservationists, and curious builders study animal-augmented sensing rigorously — with shared hardware, shared firmware, shared data formats, and reproducible experiments.
We are not a disease-screening product. We are not promising disaster prediction. We are not competing with closed clinical players. We are not claiming animals predict earthquakes.
We claim only that the platform works — and that if the open questions in this field are ever going to be answered, the answers will come from shared, open, rigorous tooling. That's what this is.
AnimalSensePipe is a Ritzy Lab open-source project. It is the sibling of SensorPipe — Ritzy Lab's industrial sensor-pipeline framework — and runs on EmbedIQ, Ritzy Lab's AI-first open-source firmware and gateway framework. Same architectural DNA, same Apache 2.0 license, same first-principles approach: open format, open hardware, observable by default.
Where SensorPipe demonstrates EmbedIQ's industrial relevance, AnimalSensePipe demonstrates its scientific and humanitarian breadth — a framework that scales from factories to wildlife. Both projects ship with reference hardware. Both are designed to be forked.
Animal welfare and ethical framing are central. Reference deployments are non-invasive (collar-worn, site-fixed) and follow established field-research protocols. The optional EEG header in v2 is a header, not a populated component — to leave room for future research while keeping the v0 kit clear of any invasive-BCI patent territory.
The repo is public. The license is Apache 2.0. The first deployment costs about $120 in parts. If you want to talk before you build — about partnerships, deployments, or where this fits your research — write us.