I have to find the best approach for tackling a problem for trying to recognize physical movements - with an iPhone in a pocket - like waling, stopping, turning left/right, sitting.
I was thinking on just heuristically find the data corresponding to each action, then to check the incoming values against this data (with a threshold) and see what's happening.
That's a very rough approach, of course, so maybe using something like the Support Vector Machine
methods, but this seems too complicated for the amount of time I have to develop this.
Which approach would you suggest here?
Walking: Do an fft on the gravity direction signal. Measure its frequency response for walking at different speeds and then set a simple threshold.
Stopping: if the average power i.e. total energy in the signal over the last few seconds drops below a certain threshold then you can say the user has stopped.
Turning left,right: Use the gravity vector and the gyroscopes rotation speed vector to determine whether the user is rotating clockwise or counterclockwise
Sitting: This will be very hard to determine but if youre lucky the SVM will find the right pattern.
Each of the above can be given a weighting and then you will have to find a good way to obtain training data to train your SVM. Maybe stream the signals from the phone to a webserver and simultaneously record the users motions by hand.
Your best starting point is Apples Sample code: CoreMotionTeapot
Alternatively you could analyze the GPS signal. This will give you a very good way to determine the users larger scale motion like walking/moving or changing heading etc.
Related
I am currently working on a mission to fuse GNSS and IMU for a more accurate navigation system for autonomous vehicles. I am very familiar with using GNSS to get the accurate position, however I'm a newbie in using IMU sensor. I've read several kinds of literature but am still confused about which better way should I do to remove bias from the accelerometer and gyroscope measurement.
I have 2 kinds of raw measurement data using MPU-9250, they are acceleration data (m/s2) in the x,y, z-axis and angular velocity data (deg/s) also in the x,y, z-axis. I have tried to input these data into my sensor fusion program. Unfortunately, I got unsatisfied with accuracy.. Hence I think firstly I should correcting (removing bias) of raw data IMU, and then the corrected IMU data can be input to my fusion program.
I couldn't find an answer that my brain could understand or fit my situation. Can someone please share some information about this? Can I use a high-pass filter or a low-pass filter in this situation?
I would really appreciate if there is someone could explain in detail to me without using complex math formulas/symbols, I'm not a mathematician and this is one of my problems when looking for information.
Thank you in advance
Accelerometer and Gyroscope have substantial bias usually. You could break the bias down to factors like,
Constant bias
Bias induced by temperature variation.
Bias instability
The static part of bias is easy to subtract out. If the unit starts from level orientation and without any movement, you could take samples for ~1s, average it and subtract it from your readings. Although, this step removes a big chuck of bias, it cannot still fully remove it (due to level not being perfect).
In case you observe that the temperature of IMU die varies during operation (even 5-10 deg matters), note down the bias and temperature (MPU9250 has an inbuilt temperature sensor). Fit a linear or quadratic curve that captures bias against temperature. Later on, use the temperature reading to estimate bias and subtract it out.
Even after implementing 1 and 2, there will still be some stubborn bias left. If the same is used in a fusion algorithm like Kalman filter (that is not formulated to estimate bias, the resulting position and orientation estimates will be biased too).
Bias can be estimated along with important states (like position) using some external reference/sensor like GNSS, Camera.
Complementary filter (low pass + high pass) or a Kalman filter can be formulated for this purpose.
Kalman filter approach:
Good amount of intuition along with some mathematics is needed to use this approach. Basically the work involves formulating prediction & measurement model and then provide rough noise variances for your measurements and prediction. An important thing to understand is that, Kalman filter assumes that the errors follow normal distribution without any bias. So the formulation should deliberately put bias terms as unknown states that should be estimated too (Do not assume that the sensor is bias free in the formulation)..
You could checkout my other answer to gain a detailed understanding of this approach.
Complementary filter approach
Complementary filter is simpler for simpler problems :P
The idea is that we use low pass filter on noisy measurement and high pass filter on biased measurement. Then add them up and call it a day.
Make sure that both the LPF and HPF are complements of each other (Transfer function of HPF should be 1-LPF). Typically first order filters with same time constants are used. Additionally the filter equations have to be converted from continuous laplace domain to discrete form (Read about ZOH, Tustins approximation...).
The final form is scattered around the internet too.
Personally I would use a Kalman filter for this purpose, but complementary filter can be used with same amount of effort. You could do this,
Assume that the body is not accelerating on average in long term (1-10 s or so). Then you could say that the accelerometer measures the direction of gravity in long term relative to the IMU. Then arctan(accy, accz) can be used to obtain an estimate of pitch and roll. But this pitch and roll readings will suffer from substantial noise. Implement a low pass filter on it with time constant ~5 seconds or so. Additionally add the latest pitch/roll with dt*transformationMatrix*gyroscope to get another pitch and roll. But these suffer from bias. Implement a HPF over gyro based Pitch and Roll. Add them together to get Pitch and Roll. Lets call these IMU_PR.
Now forget our original acceleration assumption. accelerometer gives specific force (which is net acceleration - gravity). Since we have Pitch and Roll angles (IMU_PR), we know gravities direction. Add gravity to accel readings to get an estimate of acceleration. Apply proper frame conversion to bring this acceleration to same coordinate frame as GPS (you will need an estimate of Yaw to do so. Fuse a magnetometer with gyroscope for this purpose). Then do vel = vel + acc*dt. Integrate it again to get an estimate of position from IMU. But this will drift due to the bias in accelerometer (and pitch, roll). Implement a high pass filter over this position and low pass filter over GPS position to get a final estimate.
Over the past year I have used many different methods of combining Accelerometers, gryos and Magnetometers to get accurate readings of Head angles.
I have also started looking into using a Kalman filter to further improve these readings.
Yet I am still to find a method of removing external magnetic field influences using the other sensors, for example;
If my heading angle was accurate, and suddenly an external magnetic field approaches, my heading angle will be influenced, but to my gyro and accelerometer I haven’t moved.
Is there any algorithms or calculations anyone can think of to override the magnetometer in a way that can determine whether you have moved or not?
Any help would be much appreciated!
One simple solution is to use the gyro/accelerometer as you mentioned, and combine that with delayed filtering, where you wait for a couple of seconds before providing an estimate of the attitude.
Compute the short term attitude from gyro/accel only (start with any arbitrary heading) using gyro integration with accel measurements, and then compute the short term attitude from the magnetometer/accel only using, say TRIAD. Compute the error between these two quantities and decide on a threshold. If the you exceed the threshold, it means there is a magnetic disturbance, and you can thus stop using it in your attitude solution. If they are within threshold, you can continue using the magnetometer.
If you think of more metrics to decide whether you are in a magnetic disturbance or not (such as the magnetometer norm rising to a ridiculous number), then you can add those metric to an HMM, which will combine these metrics and give you an estimate of whether you are in a disturbance or not.
I'm hoping someone will be able to tell me why no filtering is helping in my application.
I have a MEMS microphone monitoring the pressure of a small chamber, which has a membrane stretched over the far end. This device is placed on a human muscle and when I flex said muscle the membrane is disturbed, producing a pressure difference in the chamber, which the microphone picks up. Therefore, by flexing a muscle I can see nice spikes of activity. However, this method is very susceptible to noise, both motion artefacts and other undesirable artefacts.
The muscle activity I'm interested in is above 10Hz and below 100Hz, so I'm trying to bandpass (or at the very least, highpass) the noise. If I tap the device, or if I have the device on my upper forearm and tap my wrist, I'm to understand that this is a very low frequency noise, somewhere in the region of 1Hz/2Hz, but I can't get rid of this noise!
I'm using MATLAB to process. Generally I sample this microphone at 1KHz, but I currently have it hooked up to a DAQ at 5KHz sampling rate. I desperately want to get rid of this low frequency noise but nothing I try seems to make any difference, it's very hard to see what the filter is doing at all. It's definitely attenuating the signal, but not getting rid of the noise I want. I don't expect perfect results, but certainly better than what I'm seeing.
I've used lots of methods to create filters in MATLAB (manually and fdatool), along with different types of filters (Butterworth, Chebyshev, Elliptic) all not helping. I'm worried that my desired frequency of 10Hz is perhaps too close to the noise I'm trying to filter out, and it's not able to attenuate the noise enough.
Any ideas, code samples, or recommendations would be very helpful.
Tapping or percussive sounds are broad spectrum, producing frequency content well above the repeat rate of 1 Hz or so. So any linear band pass or high pass filter will not be able to completely remove this broad spectrum noise.
My project:
I'm developing a slot car with 3-axis accelerometer and gyroscope, trying to estimate the car pose (x, y, z, yaw, pitch) but I have a big problem with my vibration noise (while the car is running, the gears induce vibration and the track also gets it worse) because the noise takes values between ±4[g] (where g = 9.81 [m/s^2]) for the accelerometers, for example.
I know (because I observe it), the noise is correlated for all of my sensors
In my first attempt, I tried to work it out with a Kalman filter, but it didn't work because values of my state vectors had a really big noise.
EDIT2: In my second attempt I tried a low pass filter before the Kalman filter, but it only slowed down my system and didn't filter the low components of the noise. At this point I realized this noise might be composed of low and high frecuency components.
I was learning about adaptive filters (LMS and RLS) but I realized I don't have a noise signal and if I use one accelerometer signal to filter other axis' accelerometer, I don't get absolute values, so It doesn't work.
EDIT: I'm having problems trying to find some example code for adaptive filters. If anyone knows about something similar, I will be very thankful.
Here is my question:
Does anyone know about a filter or have any idea about how I could fix it and filter my signals correctly?
Thank you so much in advance,
XNor
PD: I apologize for any mistake I could have, english is not my mother tongue
The first thing i would do, would be to run a DFT on the sensor signal and see if there is actually a high and low frequency component of your accelerometer signals.
With a DFT you should be able to determine an optimum cutoff frequency of your lowpass/bandpass filter.
If you have a constant component on the Z axis, there is a chance that you haven't filtered out gravity. Note that if there is a significant pitch or roll this constant can be seen on your X and Y axes as well
Generally pose estimation with an accelerometer is not a good idea as you need to integrate the acceleration signals twice to get a pose. If the signal is noisy you are going to be in trouble already after a couple of seconds if the noise is not 100% evenly distributed between + and -.
If we assume that there is no noise coming from your gears, even the conversion accuracy of the Accelerometer might start to mess up your pose after a couple of minutes.
I would definately use a second sensor, eg a compass/encoder in combination with your mathematical model and combine all your sensor data in a kalmann filter(Sensor fusion).
You might also be able to derive a black box model of your noise by assuming that it is correlated with your motors RPM. (Box-jenkins/Arma/Arima).
I had similar problems with noise with low and high frequencies and I managed to decently remove it without removing good signal too by using an universal microphone shock mount. It does a good job with gyroscope too especially if you find one which fits it (or you can put it in a small case then mount it)
It basically uses elastic strings to remove shocks and vibration.
Have you tried a simple low-pass filter on the data? I'd guess that the vibration frequency is much higher than the frequencies in normal car acceleration data. At least in normal driving. Crashes might be another story...
Does anyone know if Core Location in the iPhone OS uses anything but simple vector math to calculate speed? I've read that the GPS system can provide speed measurements that can be accurate when position is not (I believe using the Doppler shifts of the signals).
I've tried and failed to see if the iPhone does this. The question is basically, does this data contain information or is it just convenience functions, using (filtered?) location data?
I suppose my question is if anyone have tried this in reality, or knows beyond what is in the documentation.
The Core Location documentation describes the speed reading thus:
This value reflects the instantaneous speed of the device in the direction of its current heading.
While not absolutely definitive, this strongly suggests that the reading is direct, rather than an interpolation of positions, which cannot be described as "instantaneous" by any reasonable definition.
The GPS system in itself is not able to provide speed measurements. The only way this can practically be done is by comparing to discrete position measurements and the time between those. It's just a matter of applying simple math to get the speed and direction traveled. More samples can be used to get a more accurate measurement.
It is not feasible to measure the speed directly by simple GPS receivers, e.g. by use of Doppler shift. This is due to the fact that each satellite itself is traveling at very high speed around the globe. Each satellite orbits the globe twice every day, resulting at a speed of almost 14000 km/hour. Since the direction of the satellite compared to the GPS unit varies depending on where it is on the sky, the difference in the measure Doppler shift would be huge compared to the Doppler shift resulting from moving of the GPS receiver itself.
I'm however not saying that this couldn't be done by very sophisticated hardware and algorithms, but the cost/benefit would probably not be worth even considering it.