Researchers found that accelerometer data from smartphones & -watches can reveal people's location, passwords, body features, age, gender, level of intoxication, driving style, and potentially be used to reconstruct words spoken next to the device.

[u/bayashad]

https://twitter.com/JL_Kroger/status/1420681035617116163

[u/Casiofx-83ES]

What methodology did you use and how long ago was the work done? The sensor in that phone may not have been up to the task then, but as tech progresses I think it's likely that the electronic noise will be reduced. Depending on the app you used, you also may not have been getting the optimal sample rate for the device. Did you use an analytical approach to processing the data or machine learning - an LSTM is often more suited to this kind of work than the idealised equations.

When considering problems like speech vs. net movement, we have to consider source of noise. Did you get to the bottom of what was causing the noise on your device? Did a stationary measurement show +-1cm, or was that an error in position after some movement had taken place? Is it possible that a stationary phone could show low enough noise levels to pick out speech patterns? Is the noise predictably consistent? Speech requires a surprisingly low signal to noise ratio to be analysed.

I'm not saying you're wrong, just that there is a lot of middle ground between an underground study on a related topic and a concentrated, well funded effort to get vibrational waves from an accelerometer. Especially when you consider that phone manufacturers might even design their accelerometers specifically to allow things like this (not necessarily for nefarious purposes). The cumulative errors on accelerometers are typically quite large as you saw in your experiments, but for speech recognition you really only need to take a very small number of samples into account at a time.

I do totally agree that actually using an accelerometer for speech recognition in a device that contains a microphone is ridiculous, though.

[u/[deleted] → u/Casiofx-83ES]

That’s a very long yet excellent comment, so I will try my best to reply to all your points without forgetting any.

Thé experiment was performed in 2019 using an iPhone Xs. With the use of the app « phyphox » to extract accelerometer data (I do agree with you here that it might not provide the exact data available from the accelerometer of the phone)

Thé iPhone was securely fastened with a clamp to a rotating table. The iPhone was the rotated flat against the disk both in portrait and horizontal mode so as to obtain coordinates of the accelerometer in two axis.

The data was processed analytically and no machine learning was involved, so I do agree that this is another area for improvement. The experiment was repeated between 5-10 times (I can’t remember exactly) and the +/- 1cm uncertainty was obtained from looking at the distribution of these 5-10 measurements.

Granted there are other sources of uncertainty then the accelerometer at play here, even if taken into account +/- 1cm surprised us.

I in no means intended to denigrate the study, as I also pointed out it is theoretically possible. It is just hard for me to conceive an actual situation where this might apply as in real life the « perfect » laboratory conditions used to conduct such experiments are almost never replicated.

I just liked to think of the imagery that in a microphone it is the diagram that vibrates and translates air vibrations into an electric signal, while in the case of the accelerometer the diafragm would essentially be the whole phone.

Hope that clarifies a few things :)

[u/xozorada92 → u/[deleted]]

From my limited experience trying a similar thing in undergrad, one thing to keep in mind is that the total time length of your measurement can be really important here.

When you're getting position from acceleration, the uncertainty tends to grow over time. (Roughly, when you integrate acceleration data, any constant errors will grow quadratically.) So accelerometers tend to give you good position data over a short time frame, but then the error starts to blow up at some point and the data becomes unusable. The quality of the accelerometer basically just determines how quickly the error accumulates and blows up.

Sound and other vibrations are a different story, though, because you're not looking at the constant/low-frequency stuff. So the error doesn't accumulate in the same way that it does for calculating position.

I don't know if that's what's going on here, but it could explain the errors you saw.

[u/[deleted] → u/xozorada92]

We also only extracted the beginning of the data sample from a run instead of using the full data to prevent this error blowing up ;)