Sri Manika Makam

AI

From 5.6% to 74%: What Ground Truth Did for Our Sleep Detector

In the first post in this series we described a rule-based sleep detector for our collar wearable: bucket the IMU stream into one-minute epochs, score each epoch by how much the acceleration is changing, and call a long enough run of quiet minutes a sleep bout. We ended that post

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What The Training Runs Taught Us About IMU Bark Detection

In our previous post we built the first version of an ML bark classifier: 28 features extracted from collar IMU windows, a RandomForest winning a model bakeoff, bark F1 of 0.714 on a held-out test set. That number looked good. It was wrong. The Number That Was Lying to

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When the model meets a new dog: fine-tuning at enrollment time

The last post ended on an open problem: we could not make phone-photo galleries work well enough to matter. Adding more enrollment images made things worse, not better. This post is about what we changed and what finally moved the numbers. The setup Before getting into results, it helps to

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Multi-Pet Identification: Rethinking How We Build the Dog Identity Gallery

In our previous post we hit around 92% identification accuracy on detected frames. That result came with a catch we had quietly set aside: the gallery was built from bowl camera images, evaluated against bowl camera images. What happens when that changes? The Problem With the Current Setup A quick

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Watching a Dog Sleep With an Accelerometer

A dog cannot tell you it slept badly. It just does, and then it is cranky, or slow on the morning walk, or off its food, and you are left guessing why. Sleep quality turns out to be one of the earlier signals that something is wrong with a dog:

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Dog Identification by Facial Landmarks: Real-World Results and Failure Analysis

In our previous post we described the multi-stage vision pipeline we built to identify dogs by their facial landmarks - covering preprocessing, embedding model training, gallery building, and real-time inference. This post covers what happened when we ran it on real camera footage and reviewed every result manually. Setup A

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

How we're 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 device for dogs. It monitors eating and drinking habits, ocular temperature, sounds, and behavioral patterns over time, turning routine

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

A collar microphone can't tell whose bark it just 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