I would like to detect when a user jumps and the intensity of that jump. I'm coming up short finding good resources for this behavior.
Is there any library which handles this ?
How easy or difficult is it to get accurate data ?
(i.e. the difference between a real jump and the user rapidly moving their phone downwards)
All you would need to do is to read the accelerometer readings. To determine the different between a jump and the user moving the phone you would detect the sudden impact. So you are sampling the rate at which the accelerometer data changes. If it rapidly changes past your threshold you create then it must be a jump and vice versa. Checkout CoreMotion
Here is a tutorial that is outdated but the generally idea is the same.
Detecting a bump (sudden impact)
Related
I'm working on an iPhone app for motorcyclist that will detect a crash after it has occurred. Currently we're in the data acquisition process and plotting graphs and looking at data. What i need to log is the forward user acceleration and tilt angle of the bike relative to bike standing upright on the road. I can get the user acceleration vector, i.e. the forward direction the rider is heading by sqrt of the x,y and z accelerometer values squared. But for the tilt angle i need a reference that is constant, so i thought lets use the gravity vector. Now, i realize that deviceMotion API has gravity and user acceleration values, where do these values come from and what do they mean? If i take the sqrt of the x,y and z squared components of the gravity will that always give me my up direct? How can i use that to find the tilt angle of the bike relative to an upright bike on the road? Thanks.
Setting aside "whiy" do this...
You need a very low-pass filter. So once the phone is put wherever-it-rides on the bike, you'll have various accelerations from maneuvers and the accel from gravity ever present in the background. That gives you an on-going vector for "down", and you can then interpret the accel data in that context... Fwd accel would tip the bike opposite of braking, so I think you could sort out fwd direction in real time too.
Very interesting idea.
Assuming that it's not a "joke question" you will need a reference point to compare with i.e. the position taken when the user clicks "starting". Then you can use cos(currentGravity.z / |referenceGravity|) with |referenceGravity| == 1 because Core Motion measures accelerations in g.
But to be honest there are a couple of problems for instance:
The device has to be in a fixed position when taking the reference frame, if you put it in a pocket and it's just moving a little bit inside, your measurement is rubbish
Hmm, the driver is dead but device is alive? Chances are good that the iPhone won't survive as well
If an app goes to the background Core Motion falls asleep and stops delivering values
It has to be an inhouse app because forget about getting approval for the app store
Or did we misunderstand you and it's just a game?
Since this is not a joke.
I would like to address the point of mount issue. How to interpret the data depends largely on how the iPhone is positioned. Some issues might not be apparent to those that don't actually ride motorcycles.
Particularly when it comes to going around curves/corners. In low speed turns the motorcycle leans but the rider does not or just leans slightly. In higher speed turns both the rider and the motorcycle lean. This could present an issue if not addressed. I won't cover all scenarios but..
For example, most modern textile motorcycle jackets have a cell phone pocket just inside on the left. If the user were to put there phone in this pocket, you could expect to see only 'accelerating' & 'braking'(~z) acceleration. In this scenario you would expect to almost never see significant amounts of side to side (~x) acceleration because the rider leans proportionally into the g-force of the turn. So while going around a curve one would expect to see an increase in (y)down from it's general 1g state. So essentially the riders torso is indexed to gravity as far as (x) measurements go.
If the device were mounted to the bike you would have to adjust for what you would expect to see given that mounting point.
As far as the heuristics of the algorithm to detect a crash go, that is very hard to define. Some crashes are like you see on television, bike flips ripping into a million pieces, that crash should be extremely easy to detect, Huh 3gs measured up... Crash! But what about simple downs?(bike lays on it's side, oops, rider gets up, picks up bike rides away) They might occur without any particularly remarkable g-forces.(with the exception of about 1g left or right on the x axis)
A couple more suggestions:
Sensitivity adjustment, maybe even with some sort of learn mode (where the user puts the device in this mode and rides, the device then records/learns average riding for that user)
An "I've stopped" or similar button; maybe the rider didn't crash, maybe he/she just broke down, it does happen and since you have some sort of ad-hoc network setup it should be easy to spread the news.
I have a GPS app that I would like to detect if the user is standing still and not moving. Using Core Location works for this, but is sometimes not accurate because new updates move and gives the illusion of speed and motion.
So, I am wondering if in addition to that, I can also use Core Motion. Is this a good idea to detect motion such as someone walking, running, driving, etc, and know when they are no longer doing that motion? Or, is Core Motion only for small movements such as tilting the device or lifting it to your ear?
I wanted to tell others who visit this question what I've learned and what I think about this approach.
I have been doing some research of my own to know whether this is possible, and more importantly, even if it is what is the battery consumption and accuracy of the location change detected. For Android though, this question was asked quite sometime back. The answer provides links to this Google Tech Talk. At 23:20, the speaker talks about how difficult it is to achieve this and the accuracy you will achieve in the results.
Even though I have to come to realize the battery consumption from sensors on the iPhone is a little lesser than in most Android phones, I still think this is a costly affair in terms of accuracy and battery consumption.
you can use the GPS with the sensor readings to distinguish between walking, running, etc. if you combine the tilt angle frequency change and the GPS speed information (you need to do some work to get some of this info of course, but thats the way to do it).
You are talking about 4 different measurements from 4 different sensors (technically more than 4 but..) -
Latitude & Longitude - from CoreLocation. It uses a mix of GPS + cell tower triangulation.
Accelerometer - the current orientation of the device in 3D space.
Gyroscope - orientation of the device on its own axis.
Magnetometer - which tells you which direction a device is point w.r.t south,north,east,west
Of all these I think only Latitude & Longitude are of use to you. Basically what you do is to make the sensitivity (i.e. the update rate from the sensor) a bit more relaxed. With some tweaking around with this you should be able to tell with good accuracy if a person is standing or moving.
I'm wanting to figure out if a user is not moving at all, walking, or running using the iPhone. I'm not trying to implement a pedometer. I just want to know around about if someone is moving briskly, slowly, or not at all. I don't need mph or anything like that.
I think the accelerometer may be able to do this for me, but I was wondering if someone knows of any tutorials or example code that might be able to point me in the right direction?
Thanks to all that reply
The accelerometer won't do you any good here - it will only capture changes in velocity.
Just track the current location periodically and calculate the speed.
There are no hard thresholds for walking vs. running motion, so you will have to experiment a bit. The AccelerometerGraph sample code should get you started on how to get and interpret accelerometer data.
The Accelerometer is good, but if the user has an iPhone 4 or iPad 2 you should use the gyroscope.
CMMotionManager and Event Handeling Guide - Motion Events
Apple Documentation is the best example you can get!
People have a different bounce in their step between walking and running which can be measured with the accelerometer, but this differs between individuals (what shoes they are wearing, what surface they are upon, what part of the body is attached to the iPhone etc.), and this motion can probably be imitated by shaking the iPhone just right while standing still.
Experiment by recording the two types of acceleration profiles, and then use some sort of pattern matching to pick the most likely profile candidate from the current recorded acceleration data.
I'm working on locating an iPhone device in 3D space.
I can use lat/long to detect physical location, I can use the magnetometer to figure out the direction they're facing, and I might be able to use the accelerometer to figure out how their device is oriented, but I can't figure out a way to get height of the device off the floor.
Specifically, I need to know if the user is squatting down, or raising their hand toward the ceiling (a different of about 2 meters/6 feet).
I posted a more detailed description of what I'm trying to do on my blog: http://pushplay.net/blog_detail.php?id=36
I would love any suggestions as to how to even fake this sort of info. I really want the sort of interactivity and movement that would require ducking and bobbing, versus just letting someone sit back and angle the phone -- kind of the way people can "cheat" playing with a Wii...
The closest I could see you getting to what you're looking for is using the accelerometer/magnetometer as an inertial tracker. You'd have to calibrate the user's initial position on startup to a "base" position, then continuously sample the sensors on a background thread to build a movement model. This post talks about boosting the default sample rate of the accelerometer functions so that you can get a pretty fine-grained picture of the user's movements.
I'm not sure this will solve your concern about people simply angling the device to produce the desired action, but you will have to strike a balance between being too strict in interpreting movements and allowing for differences in movement
The CoreLocation stuff gives you elevation aswell as lat/long, so you could potentially use that although there are some significant problems with this:
Won't work well indoors (not a problem for Sat Nav, is a problem for games)
Your users would have to "calibrate" (probably by placing the phone on the floor) each location they use!
In fact, you'd need to start keeping a list of "previously calibrated locations"... which could vary hugely just in one house (eg multiple rooms and floors). Could get in the way of the game.
Can't be used on moving transport (tranes, planes, automobiles... even walking) because elevation changing so frequently.
Therefore I'd have thought that using the accelerometer as a proxy for height is a substantially more preferable route than determining absolute elevation.
I am not intimately familiar with the iphone. But it might require a hardware add-on. (which you probably don't want to do). After thinking on this the only way I know how is through light or more specific laser. You shoot out a laser on the floor and record the time it takes to get back. It's actually not a lot to put this hardware together and I am sure the iphone has connections for peripherals. Unless osmeone can trump me, I say ther eis no way to do that with an image.
Using the accelerometer output, how do I determine if the user (iphone mounted on waist) is walking?
Looking for a good algorithm to determine if the user is walking to determine activity transitions- standing-to-walking or walking-to-standing.
please help.
Thank you for your time.
For a previous project, I tried calculating the magnitude of the acceleration vector, and just setting a threshold of about 2g, and that worked pretty well in testing. A typical (hardware) pedometer will ignore single jolts that happen more than about a second apart, which seems like a good way to filter out occasional movement that isn't "walking".
Additionally, you could automatically adjust the threshold by examining the data for a while.