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Comment by devit | Hacker News Reader

Wouldn't dead reckoning with the accelerometer and gyroscope in addition to their machine learning improve this significantly? (constraining motion along the known tunnel path when in a moving train, constraining the user to be within the train stopping rectangle when detecting a train starting to move)

Or is the hardware in smartphones too inaccurate even with the extra information?

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> Wouldn't dead reckoning with the accelerometer and gyroscope in addition to their machine learning improve this significantly?

The thing that should help the most would probably be the hall sensor/magnetometer/compass. That should output decent dead reckoning.

Doing this just with the gyroscope will work very well for short movements, but it will be close to useless on long, gentle curves. Unfortunately, those MEMS gyroscopes drift quite a lot over tens of seconds. Not a problem if you can do sensor fusion with the magnetometer (reckoning) and the accelerometer (where is "down"), but the latter can't be used on a fast train, acceleration/deceleration of the vehicle and forces in curves make finding gravity challenging. No idea how well a compass works inside a subway tunnel.

But maybe I'm wrong, I just have experience trying an "artificial horizon" app on an aircraft - and here, the accelerometer is completely useless for "down". A single maneuver with some Gs and the horizon has no idea what the pitch angle is. Noisy magnetic environment, GPS off? It also doesn't know where it's going.

I wonder how well magnetometers would work inside metal trains full of electric motors.

That is what they are doing. Dead reckoning is generally very inaccurate though, so by recognizing when they are on a train they can greatly increase accuracy because they know exactly where the train is.

Is it? The article doesn't say so.

It says they use the accelerometer for detecting the user's current mode of transportation, not the distance.

It says they use the train schedules to figure out the current location of the train, based on when it started moving and how long it's been moving.

They don't mention anything like dead reckoning anywhere, unless I missed it?

Using the IMU seems hard as true signal of the train's gradual acceleration is likely drowned out by the noise of vibrations and the person's natural movements. I guess every time the train is stopped, you can calibrate the bias of the IMU if the user is standing/sitting sufficiently still, but even then the dead reckoning would drift a lot.

If you just did a FFT on microphone and acceleration for every link in a subway system and recorded the past travel times. Seems pretty straight forward to estimate your speed vs the historical records and then predict your arrival time.

So focus on speed not acceleration.

Yeah while the solution they implemented is very interesting it doesn't actually "predict your location in a subway tunnel using your phone’s vibration signature"

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