bad Accelerometer data with vibration

Question

I am working an a bike computer app. I was hoping to work out the inclination of the slope using the accelerometer but things are not working too well.

I have put in test code getting the sensor data I am just smapeling at the UI rate and keeping a moving average over 128 samples which is about 6 seconds worth. With the phone in hand the data is good and I can calculate a good angle compared to my calibration flat vector.

With the phone mounted on the bike things are not at all good. I expect to get a good bit of noise but I was hoping that the large number of samples over the big time window would remove the vibration effects and general bike movements. Unfortunately this just is not working, the magnitude of the acceleration vector is not really staying around the 9.8 mark but is dropping lower which indicates to me that something is not right somewhere.

Here is a plot of the data from part of a test ride.

As you can see when stationary at the start the magnitude is OK but once I get going it drops. I am fairly sure the problem is vibration related I initially descend and there was heavy vibration I then climb and the vibration is less and the magnitude gets back towards 9.8 but then I drop down quickly on a bad road and the magnitude ends up less than 3.

This is with a SonyErricson Xperia Active which uses a BMA250 sensor the datasheat looks like the sensor should be capable. My only theory for the cause of the problem is that the range is set to the 2g range and the vibration is causing data to go out of range and this is causing my problems.

Has anyone seen anything like this? Has anyone got any ideas on the cause of the problem?
Is there any way to change the sensitivity that I have not found?

Additional information.

OK I logged the raw sensor data before my filtering. A very small portion presented here The major axis is in green and on the flat as I belive this should be without the vibration it should be about 8.5. There is no obvious clamping on the data but I get more below 8.5 values than above 8.5 values. Even if the sensor is set up for it's most sensative 2g range it looks like the vibration shgould be OK I have a max value here of just over 15 and a minimum of -10 well ib a +- 20 ragnge just not centered correctly on the 8.5 it should be.

I will dig out my other phone which looks to have a slightly different sensor a BMA150 and try with that but unless it is perfect I think I will have to give up on the idea.

Answer 1

I used the same handset and by coincidence the same averaging interval of 6 seconds for an application a few years ago and I don't recall seeing the behaviour in the graph.

I'm wondering whether the issue is in the way the 6 second averages are being accumulated. One problem I had is that the sampling interval was not constant but depends on how busy the processor is. A sample is acquired in the specified time but the calling of the event handler depends on the scheduler. When the processor is unloaded sampling occurs at a constant frequency but as the processor works harder the sampling frequency becomes slower and more erratic. You can write your app to keep processor load low while sampling. What we did is sample for 6 seconds, doing nothing else, then stop sampling and process the last sample set but this was only partially successful as you can't control other apps running at the same time and the scheduler is sharing processor resources across them all. On the Xperia Active I found it can occasionally go out to seconds between samples which I attributed to garbage collection in one of the JVMs. The solution for us was to time stamp each sample then run some quality checks over a sample set and discard those that failed the quality check. This is a poor solution as defining what is good enough is imprecise and when the user runs another app that uses a lot of resources most sample sets can be discarded so the app needs additional logic to handle that.

The current Android API, unavailable on the Xperia Active, should have eliminated this as samples can be batched as described at https://source.android.com/devices/sensors/hal-interface.html#batch_sensor_flags_sampling_period_maximum_report_latency .

If the algorithm assumed a particular number of samples rather than counting them and the processor worked harder as the bike went faster, though I'm not sure why it would, it would produce something like the first graph because when the bike is going downhill magnitude goes down and when going up hill it goes up. There is a lot of speculation there but a 6 second average giving a magnitude of less than 3 m/s^2 looks implausible from my experience with this sensor.

Answer 2

I suspect the accelerometer is not linear over such large G ranges. If so, and if there is any asymmetry, it will do what you see.

The solution for that is to pad the accelerometer mount a bit more, foam rubber, bungy-cord, whatever, possibly mount it on a heavier stage to filter the vibration more.

Or (not a good solution) try to model the error and compensate for it.