So why can’t you “train” Sense? We get this question a lot and on the surface it seems reasonable: Sense looks at the signature of devices in order to detect them consistently in your home, so it should follow that if you turn off everything in your home except for a single device, Sense should be able to properly identify that device in isolation and then continue to look for it in the future. Unfortunately, it’s not that simple.
It’s not a matter of Sense just knowing the “pure” signature of a device in isolation. Those signatures are unique, yes, but they’re unique in your particular home too and, moreover, they look different depending on the other devices running in your home. The common machine learning analogy for what we do is trying to pick out a particular voice in a room of people speaking. This is a pretty strong analogy, but it’s not perfect. That’s already a very hard problem, but what if that voice keeps changing in quality (in vocal range, in accent, in speed, and so on)? That’s essentially what we’re dealing with. The same device can look pretty different across homes based on other devices that are running concurrently as well as the general noisiness of your home power, which itself can vary over time. It’s tough to overstate how complicated and challenging this problem is.
With this in mind, the prospect of “training” Sense should look a lot more problematic. Sense needs to see repeated patterns, enough repeated patterns that it can consistently detect a device regardless of power line noise or any other devices running concurrently (which make the “voice” sound different). Exactly how many noise-altered iterations of a device Sense needs to see varies, but it’s much more than you would be able to comfortably tag. This explanation is avoiding the topic of resolution, where Sense looks at sub-second features like the on/off transients of a device to properly identify it. The resolution of the Power Meter is downsized to allow for a better viewing experience, but even if we showed you the full data at a 1MHz resolution, it would be incredibly challenging to manually mark the exact beginning and end of a millisecond event with any accuracy — and Sense needs accuracy. To complicate things even further, the signatures of some devices, like LEDs, can change after being turned on/off in succession. It’s a tough nut to crack, for sure!
A related question we often get is, “Why can’t we tell you what devices we have in our home and you can look for them?” @kevin1 has provided a fantastic explanation via the analogy of facial recognition:
There are really two steps to the process. First machine learning identifies the bounding boxes/ circles for all the faces in a photo(s). After that a human can tag the faces with specific names. Subsequently, machine learning can begin associating names with at least some photos. But until machine learning has identified the face as a face, there’s no value in telling it that Jack is somewhere in the photo. Some photo environments do let a user define a facial region and assign a name, but that data is entirely for human benefit and NOT used for learning, because there is no “identification” for machine learning to tag with the name. But you certainly can erase incorrect names that have been automatically associated with an identified face, form improved learning. Similar to marking a device as “not on”.
While such a survey would provide us with some useful information, and we’re looking for ways to gather this information with your help, it would benefit detection in the long term and not result in immediate detection benefits.
I hope that helps to explain why training Sense is just not a realistic option. Still, you can help furnish our data science team with data to refine the device detection process by renaming devices, marking devices as Not On, and utilizing features like Community Names. Be sure to turn on Network Identification as it can help find your networked devices, like Smart TVs. In addition, we’re pressing forward with integrations that can help your Sense monitor grab ground truth data directly from your devices, like our latest Philips Hue integration.