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Beyond Thresholds: Training an ML Bark Classifier on Dog Collar IMU Data

In our first post on the bark detection pipeline, we showed how a single threshold on jerk_3d_max gives a useful signal for detecting barks from a dog collar IMU. For any capture window where jerk_3d_max crosses 6.5 g/s, the rule calls it a bark.

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The Anatomy of an LLM Guardrail: What Runs on the Input, What Runs on the Output

Not every safety check belongs in the same place. Some only make sense on the user’s question, some only on the model’s answer, and some on both. Here is how we structure the guardrails on our veterinary chatbot — and why the input/output split matters. A guardrail isn’

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Identifying Dogs by Facial Landmarks: Building a Multi-Stage Vision Pipeline

How we are building a dog facial recognition system from scratch using landmark alignment, metric learning, and a custom-trained embedding model The Problem EverBowl is Hoomanely's precision healthcare deivce for dogs. The bowl monitors eating and drinking habits, ocular temperature, sounds and behavioral patterns over time, turning routine

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Why We Chose Radar Over Lidar for Presence Detection

Presence detection sounds solved—motion sensors have shipped for decades. But a real living-space sensor must spot a person sitting dead still, ignore furniture, work in the dark, and not confuse the dog for a human. Our first trigger, a single-channel time-of-flight lidar, failed nearly all of these. Here'

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Is That Our Dog? Using IMU to Verify What the Collar Microphone Hears

A collar microphone cannot tell whose bark it heard. It picks up every sound within range — the dog wearing it, the beagle three doors down, the TV in the background. An audio classifier running on the firmware can label a sound as "bark," but it has no way

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Counting Dog Steps Offline: One Unified Pipeline for a Collar Pedometer

A step counter on a dog's collar sounds like it should be a solved problem. After all, human pedometers have shipped in phones and watches for over a decade. But a dog is not a human walking upright with a phone in their pocket. The sensor sits on

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Ports That Compile, Run, and Quietly Lie

A port that compiles and produces plausible output is not the same as a correct port. The hardest bugs in an ML migration don't crash. They degrade gracefully. After porting the Everbowl device's image and inference pipeline from Python to C++, we discovered three of them.

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How We Ran Our PyTorch Models Without Python Using TorchScript

At Hoomanely, a lot of our intelligence runs on models we trained in PyTorch: YOLO segmentation networks that understand what is happening in a frame, a MobileNetV3 classifier that recognises bowls, and a YAMNet-based audio model that listens for specific sounds. Training these in Python is comfortable. Shipping them is

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What Porting an ML Pipeline to C++ Actually Changed

Every Everbowl device sits in someone's home. It watches a dog eat, drink, move, and breathe — tracking feeding patterns, drinking patterns, eye temperature, and ambient sound to keep a quiet, continuous check on the pet's health. It is expected to run reliably for years, update silently

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Knowledge Graphs at Hoomanely: How We Built an Alpha Layer Over RAG Retrieval

When teams build their first Retrieval-Augmented Generation (RAG) chatbot, most of the engineering effort goes into chunking, embeddings, and vector search. That is exactly where we started at Hoomanely. But after months of iterating on a pet-health assistant grounded in veterinary books, nutrition manuals, and clinical PDFs, we hit a