I am trying to make a very simple Android pedometer, but so far it's failing pretty badly. I got some advice here and there on the internet, but nothing seems to be working.
I basically set an acceleration sensor and get the values of the x, y and z axis. After that I calculate their distance from the origin, which is basically:
d = sqrt(x²+y²+z²) followed by the calculation of their moving average. My idea was whenever I find a local peak I should count as a step. The issue is, I have no idea how to find the local peak right away in order to count the step. I am sorry if this seems like a simple problem, but I really have no idea how to go on from here.
Thank you very much.
I tried to implement this and the approach you take is subject to substantial measurement errors. You should just accept it. The reasons are:
a phone can be in any location, not only the trousers' pocket
phone accelerators are not medically precise, and they can deviate and "flow" given exactly the same position in space
moving average is not the best known technique to do this, a better one would use some sort of waves and wavelet analysis
One step has two local maximums and two local minimums (if I remember correctly)
There is no strict definition of a "step" globally accepted, this is due to physiology, measurements and various techniques used in the research field
Now to your question:
Plot the signal from the three axis you have, this will dramatically help you (signal vs time)
Define a window of a fixed (or slightly moving) size, moving window is required to detect people who walk slower, run or have disability
Every time you have a new measurement (usual frequency is about 20-30 Hz), put one to the tail of the window (your signal measurement's queue) and pop one from the head. In this way you will always have a queue with the last N measurements
Again for every mesurements recalculate your stuff and decide if the window contains one (or two!) minimums and count it as a step
good luck!
Related
I am new to Android and not familiar with the different type of sensors. I am working on an App and part of it has to count the number of speedbreakers that a car will pass over during its journey.
The phone will remain stationary in one position
I have tried using the accelerometer and tried to use a peak in vertical acceleration followed by a negative vertical acceleration as an indicator of a speedbreaker but there is too much fluctuation for an accurate result.
Here speedbreakers are smooth slopes of cement, usually a few inches high.
Any help or guidance would be greatly appreciated.
I would start out with logging the accelerometer data (probably the speed data (from gps) as well) and manually marking the points where you pass a speedbreaker.
Then the first step would be to see if there is something to see here: maybe there is a clear 'signal' that's apart from the normal fluctuation, but you haven't gotten the tweaking right.
If there isn't, you can always see if there is something there that you haven't recognized. Some sort of of normal behaviour that stops for a bit. These can be harder to detect visually so you'd have to do something with the signal.
If you know nothing about signal processing it might be tricky, but as a random starting point, read up on how step-detection works: https://en.wikipedia.org/wiki/Step_detection
Some of the methods might be usefull to you. Look at the FFT, process your signal to filter out the points you need. Maybe even train a simple network to see if it finds anything going on at your desired points?
I want to detect a specific pattern of motion on an Android mobile phone, e.g. if I do five sit-stands.
[Note: I am currently detecting the motion but the motion in all direction is the same.]
What I need is:
I need to differentiate the motion downward, upward, forward and backward.
I need to find the height of the mobile phone from ground level (and the height of the person holding it).
Is there any sample project which has pattern motion detection implemented?
This isn't impossible, but it may not be extremely accurate, given that the accuracy of the accelerometer and gyroscopes in phones have improved a lot.
What your app will doing is taking sensor data, and doing a regression analysis.
1) You will need to build a model of data that you classify as five sit and stands. This could be done by asking the user to do five sit and stands, or by loading the app with a more fine-tuned model from data that you've collected beforehand. There may be tricks you could do, such as loading several models of people with different heights, and asking the user to submit their own height in the app, to use the best model.
2) When run, your app will be trying to fit the data from the sensors (Android has great libraries for this), to the model that you've made. Hopefully, when the user performs five sit-stands, he will generate a set of motion data similar enough to your definition of five sit-stands that your algorithm accepts it as such.
A lot of the work here is assembling and classifying your model, and playing with it until you get an acceptable accuracy. Focus on what makes a stand-sit unique to other up and down motions - For instance, there might be a telltale sign of extending the legs in the data, followed by a different shape for straightening up fully. Or, if you expect the phone to be in the pocket, you may not have a lot of rotational motion, so you can reject test sets that registered lots of change from the gyroscope.
It is impossible. You can recognize downward and upward comparing acceleration with main gravity force but how do you know is your phone is in the back pocket when you rise or just in your waving hand when you say hello? Was if 5 stand ups or 5 hellos?
Forward and backward are even more unpredictable. What is forward for upside-down phone? What if forward at all from phone point of view?
And ground level as well as height are completely out of measurement. Phone will move and produce accelerations in exact way for dwarf or giant - it more depends on person behavior or motionless then on height.
It's a topic of research and probably I'm way too late to post it here, but I'm foraging the literature anyway, so what?
All kind of machine learning approaches have been set on the issue, I'll mention some on the way. Andy Ng's MOOC on machine learning gives you an entry point to the field and into Matlab/Octave that you instantly can put to practice, it demystifies the monsters too ("Support vector machine").
I'd like to detect if somebody is drunk from phone acceleration and maybe angle, therefore I'm flirting with neuronal networks for the issue (they're good for every issue basically, if you can afford the hardware), since I don't want to assume pre-defined patterns to look for.
Your task could be approached pattern based it seems, an approach applied to classify golf play motions, dancing, behavioural every day walking patterns, and two times drunk driving detection where one addresses the issue of finding a base line for what actually is longitudinal motion as opposed to every other direction, which maybe could contribute to find the baselines you need, like what is the ground level.
It is a dense shrub of aspects and approaches, below just some more.
Lim e.a. 2009: Real-time End Point Detection Specialized for Acceleration Signal
He & Yin 2009: Activity Recognition from acceleration data Based on
Discrete Consine Transform and SVM
Dhoble e.a. 2012: Online Spatio-Temporal Pattern Recognition with Evolving Spiking Neural Networks utilising Address Event Representation, Rank Order, and Temporal Spike Learning
Panagiotakis e.a.: Temporal segmentation and seamless stitching of motion patterns for synthesizing novel animations of periodic dances
This one uses visual data, but walks you through a matlab implementation of a neuronal network classifier:
Symeonidis 2000: Hand Gesture Recognition Using Neural Networks
I do not necessarily agree with Alex's response. This is possible (although maybe not as accurate as you would like) using accelerometer, device rotation and ALOT of trial/error and data mining.
The way I see that this can work is by defining a specific way that the user holds the device (or the device is locked and positioned on the users' body). As they go through the motions the orientation combined with acceleration and time will determine what sort of motion is being performed. You will need to use class objects like OrientationEventListener, SensorEventListener, SensorManager, Sensor and various timers e.g. Runnables or TimerTasks.
From there, you need to gather a lot of data. Observe, record and study what the numbers are for doing specific actions, and then come up with a range of values that define each movement and sub-movements. What I mean by sub-movements is, maybe a situp has five parts:
1) Rest position where phone orientation is x-value at time x
2) Situp started where phone orientation is range of y-values at time y (greater than x)
3) Situp is at final position where phone orientation is range of z-values at time z (greater than y)
4) Situp is in rebound (the user is falling back down to the floor) where phone orientation is range of y-values at time v (greater than z)
5) Situp is back at rest position where phone orientation is x-value at time n (greatest and final time)
Add acceleration to this as well, because there are certain circumstances where acceleration can be assumed. For example, my hypothesis is that people perform the actual situp (steps 1-3 in my above breakdown) at a faster acceleration than when they are falling back. In general, most people fall slower because they cannot see what's behind them. That can also be used as an additional condition to determine the direction of the user. This is probably not true for all cases, however, which is why your data mining is necessary. Because I can also hypothesize that if someone has done many situps, that final situp is very slow and then they just collapse back down to rest position due to exhaustion. In this case the acceleration will be opposite of my initial hypothesis.
Lastly, check out Motion Sensors: http://developer.android.com/guide/topics/sensors/sensors_motion.html
All in all, it is really a numbers game combined with your own "guestimation". But you might be surprised at how well it works. Perhaps (hopefully) good enough for your purposes.
Good luck!
I'm trying to make an Android application that uses a smartphone moved along on a flat surface (e.g. a desk) as a mouse. Since I want to emulate a mouse, I ignore the z-axis, and figure that the best way to utilize the accelerometer data would be to construct a two dimensional vector that I could then scale to the size of the screen.
I've read other answers on SO and I see that the integration method has a large error as t increases, but I'm not sure if this error is a factor considering the short duration and position change of mouse movements (How long is the average mouse movement? I'd assume less than 2 sec.).
How would I go about designing an algorithm that meets my needs? Is an integration-based algorithm sufficient?
Yes, an accelerometer data have high mistake, that would create a large errors if we'll try to get absolute coordinates out of them. But a mouse needs no absolute coordinates. Relative ones are absolutely enough. Use your integration, not a doubt in it.
"the integration method has a large error as t increases" - correct, but a user is really interested in the last movement only. So, it will work as a mouse, and it will be felt as a normal mouse. How good the mouse will be, is up to the concrete device and the task. I am not at all sure about serious gaming, for example. You will have to do your own survey about it. But it will do really a very bad tablet/pen simulator.
Be careful about ignoring the Z axis, for notice, even for placing a point on the map GPS uses all three coordinates - for better precision. Often movements will not have Z change equal to 0. And simply ignoring one of the coordinates, instead of recounting all three of them into two you really need, will cause greater mistakes. I am not sure you can allow it. And you simply needn't - it is NOT a heavy algorithm, devouring much time and battery. And for a user the possibility to move the device in the air could bring much convenience - not everybody wants to scratch his device against a table. So, COUNT two coordinates from three source ones, but not simply GET two of the source ones, ignoring the third.
The problem will be elsewhere. When you use mouse and an error collected, you can raise the mouse up and move it to another point and start from it anew. You should realize something similar, too, for your device will collect errors in time as well.
I am trying to create an application that will track movement of the device in 2D space. After doing research online, all I could find that one way to do it is integrate linear acceleration twice but the error is horrible.
Are there any solutions to this problem? I would like to be able to move my phone up, which would cause a vertical line to be drawn on the screen, to scale of how far the phone was moved. Then if I move the phone to the left, horizontal line would be drawn - effectively allowing me to draw on the screen using movements of the phone.
Can this be done at all? If so, what direction should I take in the development? I don't know where to start...
EDIT: More about the project:
I am trying to make an exercise app that will track the movement of the leg/arm: for example, when you are doing stomach crunches and the phone is attached with an armstrap to your ankle.
The app would track repeated movements of the leg.
Unfortunately the accelerometers in these phones are nowhere near what you need to implement an inertial measurement unit. The big problem is since you are integrating twice an integration always comes with a constant integral(x,dx) = x^2/2 +c this constant is what makes this difficult. To make things worse you get it twice, once when integrating to get velocity and once to get position.
One method of fixing this that I have seen in commercial innertial measurement units is called a zero velocity null, this is where you use some other source of data to tell it when you have stopped the motion of the device so you can zero out the velocity. For example I saw a project put an inertial measurement unit on a shoe and it would zero the velocity whenever it detected the shoe being put on the ground which vastly improved the accuracy. Its possible that you could use a camera or something to determine this, however I have not seen it done. If you would like to start messing with this then you are an awesome person and I would love to hear how it turns out.
Edit: I should clarify that the constant I mention above is where the error accumulates. If you can zero velocity null it then you periodically drop the accumulated error from your stored current velocity. The error in position will still accumulate, however this would make it not drift when they are holding it relatively still which may make it passable for drawing.
I know no other way other than integrating the acceleration twice.
Moreover I think that it's not possible if you don't have knowledge about other sensors that might be in your device (for example on one of my devices I have 7 (seven) sensors related to various physical signals the device might be receiving).
Other than that remember that the sensor data is noisy and almost always must be pre-filtered. For example you can use geometric mean of last 10 samples. That should lower your error by providing a smoother input data to the integrating function.
Considering I have two smartphones, A and B. If I am holding smartphone A, is there a way to determine the location of B in relation to myself?
So if we had the situation of this image:
:
it would tell me B is at position (2, 1).
Inventive methods like using the strength of wifi signals to get position are more then welcomed. Could I also determine if there is a wall between the two phones?
As far as I understand, both Bluetooth and Wi-Fi signals move in a radial wave in all directions - so while you may be able to measure the distance between the two terminals, I doubt this would give you a correct reading, since it could either be on your "side" of the circular area or another one equidistant to the source of the signal.
While GPS may be the obvious solution since it provides exactly what you're looking for, I'm not sure if you're including this as an option. Once you get the two coordinate sets for the devices, it's a matter of calculating the offset (N/S and E/W) from device 1.
This makes me think on the accuracy given by GPS, which considering that you were including the tag Bluetooth in the question and since Bluetooth has a range of around 15-30 feet (type 2-3) and the GPS has an error margin of 25-35 feet, this may not be good either.
If you do manage to get a connection (Bluetooth) between the two devices, you'd already know your in that range, but not in what direction. You can get a signal strength measure from Android 2.1: How do I poll the RSSI value of an existing Bluetooth connection? but there-again I'm not sure as to how to detect in what direction the user is relative to you, just how close he is, in any direction. Hell the other device could be on top of you or under you and you'd get virtually the same reading as next to you given equal distances.
This is all on a "static" approach, meaning both devices are stationary. However if you measure that value and then take a step to your left and re-measure you can tell if you're closer or further away from the source, so with a little trial and error of one of the devices moving you could determine a relative position, this however may not be useful for you since you'd either need to tell the phone manually that you moved left and to re-measure or use something more complicated like monitoring the accelerometer of the phone which could tell in what direction the phone moved and map the strength of the signal.
Am I losing my mind? Probably.
No answer as far as I'm concerned for now, just thoughts. Depending on what the application will do, there may be other viable approaches. This is my brain-dump answer so hopefully someone else can read it and come up with a more elaborate answer instead of rumbling thoughts.
If the distance from A to B is more than a few metres, then if you can get the GPS location of both A & B, you can easily calculate distance and bearing between them using the Location.distanceTo() and Location.bearingTo() methods.
Depending on the physical environment, it is probable that two GPSs which are physically close together will be using the same satellites for calculation, and that the errors will be similar in both. So in some cases using GPS may work for small distances.