You sound very confident your device would have worked really well. I’m curious, how much testing did you do?
I have a Garmin Vivosmart 3 and it tries to detect when I’m either running, biking, or going up stairs. It works amazingly well considering the tiny amount of hardware and battery power it has, but it also fails sometimes, like randomly thinking I’ve been running for a while when I’ve been doing some other high heart rate thing. Maddeningly, I can’t figure out how to turn off some of the alerts, like when I’ve met my “stair goal” for the day.
Only eating with a fork. A full system would require more data than that. We tested on real people in real-world conditions who were not part of the training dataset. If someone ate in a different style we could add just a little bit of annotated training data for the eating style, run the toolchain overnight and the algorithm would be noticeably better for that person and everyone else. The reason why I’m so confident in our algorith was because ① it required very little data to do updates and ② I had lots of experience in the field which meant I knew exactly what quality level was and wasn’t acceptable to customers.
To update the code in response to user feedback we would have to push the new code. Building an update system was theoretically straightforward. It was a (theoretically) solved problem with little technical risk. But it was not a problem that we had personally built a toolchain for and the whole firmware update system involved more technical maintenance than I wanted to commit myself to.
You sound very confident your device would have worked really well. I’m curious, how much testing did you do?
I have a Garmin Vivosmart 3 and it tries to detect when I’m either running, biking, or going up stairs. It works amazingly well considering the tiny amount of hardware and battery power it has, but it also fails sometimes, like randomly thinking I’ve been running for a while when I’ve been doing some other high heart rate thing. Maddeningly, I can’t figure out how to turn off some of the alerts, like when I’ve met my “stair goal” for the day.
Only eating with a fork. A full system would require more data than that. We tested on real people in real-world conditions who were not part of the training dataset. If someone ate in a different style we could add just a little bit of annotated training data for the eating style, run the toolchain overnight and the algorithm would be noticeably better for that person and everyone else. The reason why I’m so confident in our algorith was because ① it required very little data to do updates and ② I had lots of experience in the field which meant I knew exactly what quality level was and wasn’t acceptable to customers.
To update the code in response to user feedback we would have to push the new code. Building an update system was theoretically straightforward. It was a (theoretically) solved problem with little technical risk. But it was not a problem that we had personally built a toolchain for and the whole firmware update system involved more technical maintenance than I wanted to commit myself to.