"Why is Max scratching more this week?" Somewhere in Max's data, the makings of an answer exist: his bowl logged a food switch on July 5, the new kibble's ingredient list includes chicken meal, his tracker has flagged more nighttime restlessness since July 8,
Three different sources have something to say about Max's appetite this month. His smart bowl has logged five smaller-than-usual breakfasts. A vet note mentions "monitor appetite." A dog-care book says goldens often eat less in summer heat. Do we know Max's appetite has dropped?
A smart bowl reading looks like this: 120 g served, 82 g eaten, 340 seconds, 7:14 a.m. It is perfectly precise and almost meaningless on its own. A vet note is the opposite: rich with meaning ("mild tartar, monitor appetite"), structured not at all. To answer
Our IMU bark classifier now has 11 training generations behind it. This post covers what changed between v8 (our last reported state) and v11 (live as of August 5), introduces a real ambient audio clip where the audio classifier was completely fooled but the IMU was not, and explains the
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
At 7:14 this morning, a smart feeding bowl logged something small: Max, a six-year-old golden retriever, left a third of his breakfast. His collar tracker says yesterday's walk was completely normal. A vet note from two weeks ago mentions mild tartar buildup. And a dog-nutrition book on
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
Our IMU-based activity tracker clips to a dog's collar and, in principle, can tell you how far your dog walked today. In practice it needs a teacher. An accelerometer strapped to a moving neck produces a beautiful, noisy waltz of a signal, and turning that waltz into a
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
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