This is a general question seeking advice on the pattern required to calculate a user's velocity / pace / speed, when running or swimming.
Specifically, I want to be able to calculate this from watch OS, disconnected from the companion phone.
With GPS capabilities of Watch 3 / Watch OS 10.0 would the best approach be to:
Start Location Manager
Calculate distance and time between location points...
Calculate average speed?
Or are there better alternatives?
There is a good article here https://www.bignerdranch.com/blog/watchkit-2-hardware-bits-the-accelerometer/ that recommends using CoreMotion for device speed. However, this in my view would rather represent the 'device-speed' and not necessarily the user's speed over distance.
Any advice or experiences would be much appreciated.
Thanks.
The article you linked to is for WatchOS 2, not Watch 2. The motion tracking is pretty good, but to get accurate device speed you will still need to use the GPS.
If you don't need to do any other location related calculations, and don't need real time data (EDIT you can get near real time data with an HKAnchoredObjectQuery. This is sufficient for most situations) then you don't need to start location manager, just an HKWorkoutSession. This will default to using the GPS or Motion Data (which ever is more accurate/available at the time) and manage everything for you. When the workout is over, you can query for the distance samples and calculate pace from that.
If you need live motion data then the steps you outlined are correct, however you should check that the user is outdoors first. If the user is indoors or has a weak GPS signal switch to using Motion Data (and be sure to set the HKMetadataKeyIndoorWorkout appropriately if using HealthKit).
Related
For one of our projects i'm looking for a way to only let centrals (native iOS or Android app) connect with the perhiperal when they are inside a defined connection range (distance around the perhiperal).
I know that BLE is not designed for distance measuring but i hoped there is a reliable way to make a difference between centrals in a range <2m from the perhiperal and centrals >3,5 meter from the perhiperal.This means i do not need to measure the exact distance.
An important thing to mentoin is that our perhiperal can be located in an "open field" situation but also in situations where it is surounded with walls or concrete for example in a entry floor of a building or a carpark.
Another possible issue is that the central can be inside a car but if this is the case, all centrals for the concerning perhiperal are inside a car. Ofcourse it can be different cars.
Note that there is max 1 perhiperal at the time inside the connection range.
In our current version we developed an formulla what uses the received RSSI strength to estimate the distance. Unfortunately we cannot get this working reliable enough. Maybe we need to use another formulla or calibration method or whatever, we really tried many things during the last 6 months.
The concrete question is:
Is it technically possible to achieve the target as described above and when yes, what is the way to achieve the target above? We are open for specific BLE antenna's or specific designed casings for the BLE antenna or whatever is needed. It is also okay when we need to build an calibration application or specific hardware to calibrate our perhiperals, for each perhiperal, so we are realy open minded for any solution as long as it works reliable!
When more info is required to give an answere, please let me know what is missing and i will complete the info.
Unfortunately, it is like you said, you cannot use Bluetooth or Bluetooth Low Energy for short distance/range measurements. Bluetooth was just not designed that way. You want to get accurate reliable measurement for something between 2-3.5, this is way too small for BLE to be capable of that. I know that this is not what you want to hear, but I have already tried this and wasted many months on this before.
The only thing I can recommend if you really really need to continue down this path is that in order to get a more reliable outcome, you will need many many devices that are measuring the RSSI simultaneously, and then those devices need to be talking to each other to get an average RSSI measurement. You may also want to look at configuring the Tx power based on the average readings that you get, i.e. the closer the device gets, you lower the Tx power of both the scanning and advertising devices. Finally, directional antennas can be used if you are planning to use non-Android non-iOS devices for scanning, but this will be tricky if the only antenna you can change is that of the peripheral.
My application is EXTREMELY dependent on user location, so much so that accuracy is very crucial to the use of the app. Speaking with my team we have realized the scenario that if a user is in close proximity to another geofence that we have created, CoreLocation may not recognize the difference. Are there currently any frameworks that work better for CL or LocationManager in iOS. I check the Cocoapods library but they all seem pretty close to one another in functionality.
You cannot overcome the physical and technological limitations of the GPS system. If you call CLLocationManager's requestLocation when the location manager is configured with the highest desired degree of desiredAccuracy (namely kCLLocationAccuracyBestForNavigation), the response in locationManager(_:didUpdateLocations:) is as accurate is you are going to get. Using a "library" is not going to change that.
You can set the accuracy to kCLLocationAccuracyBestForNavigation and then you will get the best data available. If you are not happy with iOS handling of geofences you could try to implement the geofence thing yourself by just subscribing to the delegate methods.
That way you can also filter CLLocations with a low accuracy and ignore them for example.
As others have said, there no magic bullet to give you accuracy better than what the GPS is able to provide.
What you can do is
Set the desiredAccuracy to the max
Check the accuracy value you get back from location updates, and reject readings with a horizontal "accuracy" value that is too large (since the "accuracy" reading is actually a "circle of confusion" or radius in meters where the phone may actually be located.
Note that step 2 may cause it to take a LONG time to get an acceptable location fix, or you may not be able to get an acceptable reading at all. Based on the testing I did several years ago, requiring a horizontal accuracy reading of 16 meters was the best I was able to achieve, and I could only get that signal quality in an open area free of buildings and without a lot of tree cover. (I haven't tried it in an open prairie, however.)
I would like to develop a mobile app for iPhones, that calculates time needed to reach a given velocity. For example: I'm in my car, open the app, choose 100km/h and when I accelerate the app should start to count time and it stops counting just in the moment when I reach 100km/h. It should be very accurate.
I heard about two solutions. First is to use the accelerometer/gyroscope, but some people told me it's bad idea, because I won't be able to calculate time on longer distances. The second option is to use GPS, but on the other hand it can be not as accurate as I want it to be.
So I need suggestions, which option is better and why.
My targets are iPhones 4s and newer.
If you want to be more precise than the GPS you will need to have some sort of sensor. Most similar apps and concepts will create a receiver that plugs into the car that the iPhone can connect to. This has the benefit of making all of the sensors in the car available to you. This is an example: https://www.automatic.com/how-automatic-works/
I've been researching the new M7 chip's CMMotionActivityManager, for determining whether the user of the device is walking, running, in a car, etc (see Apple Documentation). This seemed like a great step forward over trying to determine this previous from using LocationManager and accelerometer data only.
I notice however that CMMotionActivityManager does not have a cycling activity, which is disappointing, and almost a deal-breaker for complete usage as a new activity manager. Has anyone else found a convenient way to use CMMotionActivityManager with cycling also without having to reincorporate CMLocationManager + accelerometer just to try to test for cycling too?
Note, this also does not include general transport options for things like a Train. For instance, I commute an hour a day on the train. Automotive could be made more generic at least, similar to how Moves uses Transport.
CMMotionActivity has these defined motion types only:
stationary
walking
running
automotive
unknown
Useful notes from Apple's code, that does not necessarily solve the issue, but is helpful:
CMMotionActivity
An estimate of the user's activity based on the motion of the device.
The activity is exposed as a set of properties, the properties are not
mutually exclusive.
For example, if you're in a car stopped at a stop sign the state might
look like:
stationary = YES, walking = NO, running = NO, automotive = YES
Or a moving vehicle, stationary = NO, walking = NO, running = NO,
automotive = YES
Or the device could be in motion but not walking or in a vehicle.
stationary = NO, walking = NO, running = NO, automotive = NO. Note in this case all of the properties are NO.
[Direct Source: Apple iOS Framework, CoreMotion/CMMotionActivity.h #interface CMMotionActivity, inline code comments]
First of all its your question or kind of informative details on M7?
Has anyone else found a convenient way to use CMMotionActivityManager
with cycling also without having to reincorporate LocationManager +
accelerometer just to try to test for cycling too?
See there is lots of confusion it will create if you want to check if activity is type of cycling ??because its just depend on accelerometer
accelerometer contain microscopic crystal structures that get stressed by accelerative forces, which causes a voltage to be generated.and from that voltage it can parse the result.. so what i know is its just classifies your speed and giving you result that its running walking or automotive so if you want to use cycling some time very fast very slow or medium so may be it will some time result in to walking or running or may be auotomotive so m7 can not clarify the thing if its automotive or cycling or running because there is not much of speed variance while you cycling.
Still while using for running and walking its some time gives wrong results in some cases.. so that will chances that your app will give wrong information too.
One more thing you asked is
Note, this also does not include general transport options for things
like a Train. For instance, I commute an hour a day on the train.
Automotive could be made more generic at least, similar to how Moves
uses Transport.
So Apple is also working on other mapping features. Apple is said to be planning notable updates to its Maps app in iOS 8, and the company is currently working on implementing both public transit directions and indoor mapping features (which Google already has on iOS).
http://www.macrumors.com/2013/09/12/apple-working-to-leverage-new-m7-motion-sensing-chip-for-mapping-improvements/ (Useful Link)
So, not sure if you still need an answer to that but here is the latest from iOs8 SDK
#property(readonly, nonatomic) BOOL cycling NS_AVAILABLE(NA, 8_0);
In session 612 at WWDC 2014, the two presenting Apple engineers provided some information: In the slides they stated:
Performance is very sensitive to location
Works best if device is worn on upper arm Best for retrospective use cases
Longest latency
Best for retrospective use cases
In the video they explain on the audio track (starting at about 11:00) that
Cycling is new, something we introduced in iOS 8.
Cycling is very challenging, and again you need the dynamics and so
it's going to be very sensitive to location.
If it was mounted on the upper arm the latency is going to be fairly
reasonable.
And if it's anywhere else, it's going to take a lot longer. So definitely I would not suggest using cycling activity classification as a hint for the context here and now. It's really something that you'll want to use in a retrospective manner for a journaling app, for example.
I made a simple test setup for iOS 8 and 9 and iPhone 5s and 6 and cycling was not detected - not a single time in over 1.5h cycling. If the new iPhone 6S makes good this major deficit in motion activity detection is unclear - Phil Schiller announced it in September 2015.
tl;tr
Currently, cycling detection in CoreMotion does not work as it works for stationary, walking, running, and car! It will be not detected and can be used retrospectively only.
Lets say we have a starting point, (x,y). By using iOS navigation can we tell how far from that starting point we moved to another location (a,b). So if i walked 20 feet in a certain direction after starting would it be able to tell me how far I've moved and in which direction?
If this technology exists can I get info on where to start learning about it?
This also needs to be done without GPS, sorry.
As rmaddy mentioned With core location class and incorporating GPS in a project you can obtain a distance traveled by the person who is walking. I found a great step by step tutorial for you which has a sample project you can build and take a look at. Here is the link..http://www.perspecdev.com/blog/2012/02/22/using-corelocation-on-ios-to-track-a-users-distance-and-speed/
Also here is the link to core location class reference for further study....https://developer.apple.com/library/iOS/documentation/CoreLocation/Reference/CLLocation_Class/CLLocation/CLLocation.html
No, you can't determine location changes accurately without GPS, even with GPS it is difficult to accuracy measure position change as small as 20 feet (GPS 5m accuracy means a +/-15 foot error)
In theory you might be able to write software to create an Internal Navigation System using the built in accelerometers, gyros, and magnetometers, but in practice they are too noisy and have too much error for this kind of use (see this question). A better rocket scientist than me might be able to make it work but it was also need to use the GPS to keep it from drifting. The M7 chip on the 5S might make this feasible.