Add Charts to your iOS App

Paddle Mate has some charts, as I mentioned earlier I am using ios-charts by Daniel Cohen. The article that put me onto ios-charts was How to Use iOS Charts API to Create Beautiful Charts in Swift. It is well worth the read if you are looking to add charts to your app.

One thing I have noticed is large spikes in memory when zooming in or panning around the chart. That is something to keep an eye on in your app.

Also great about this library is that it is a port of the Android library MPAndroidChart by Philipp Jahoda, which means you only need to learn one charting library that you can use on two platforms since the code is very similar. Handy if you are supporting both Android and iOS.

Machine Learning Research for Paddling Stroke Detection

Part of the rationale behind the creation of Paddle Mate has been to get a better understanding of what Machine Learning really is. The best way to achieve an understanding of something is to do it, hence I came up with Paddle Mate as a platform to build on as I teach it to detect paddle strokes.

Here is my understanding of Machine Learning, in beginners words.

• Create an algorithm.
• Create a program to exercise your algorithm.
• Tell the program the algorithm's parameters it can change.
• Feed the program some samples to run against the algorithm and mark the points of interest.
• Next, let the program loose on your algorithm, with more sample sets, letting the program tweak the parameters of the algorithm to find the optimal values to derive the expected outcome of the sample sets.

Ok, but how the hell do you do that?

At first, as I've covered in past posts, I had to sit and play with the sample sets to find patterns that I could boil down into an algorithm that would detect strokes. Next, I had to figure out what values I could tweak to make the stroke detection more accurate. Then I manually tweaked those values until I found some optimal values for my stroke detection algorithm.

As we can see, at the moment I am the machine. Next it will be time to replace my manual work with the machine part of machine learning.

In that vein, I have done some research into what I need to do to get the Machine learning aspect running after I finish prepping Paddle Mate for release.

Why get ready to ship Paddle Mate if it is not perfect? I want to collect more samples from users who opt in and send their kayaking sensor data to me. I can then start to improve Paddle Mate with a wider set of samples. This will really let me exercise the machine learning aspect to improve my algorithm.

Ok, here are some things I have found in my research. I am putting them here so I can refer back to them later.

• Learn R: R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.  - From the An Introduction to R: 1.1 The R environment
• R for Mac OS X Developer's Page
• R Manuals
• Not sure if I will need this but here is Rcpp for optimizing sections of an R program with C++.
• R-Bloggers: R News and Tutorials where I found:
• In-depth introduction to machine learning in 15 hours of expert videos which points to:
• An Introduction to Elements of Statistical Learning with Applications in R is a Stanford course available for free online.

Now why did I choose R?

• It is free.
• I know how to program so learning another programming language does not daunt me.
• It has good source material (the Introduction to Elements of Statistical Learning book and videos) which I can use to learn this.
• Did I mention it is free? Have you looked into the cost of Matlab or Mathmatica? Screw that, I don't need a second mortgage to learn this stuff.

Ok, back to productizing Paddle Mate.

Designing Paddle Mate

Programming is my strong suit and designing does not come naturally. Though I am inclined to art and have both drawn and painted, I still find app design is something I struggle to get right.

With that in mind, here are some things I am doing as I work to make Paddle Mate useful (and non-ugly) for users in bright sunlight, while kayaking in the evening, and when reviewing past kayaks or showing the app to a friend.

I am doing some reading first off. Here are some things I have read recently on design:

• 7 Rules for Creating Gorgeous UI (Part 1)
• 7 Rules for Creating Gorgeous UI (Part 2)
• The Easy Guide To HSL Color In CSS3 - discussing how to use Hue, Saturation, and Lightness and how to understand it.
• Color Theory For Designers: Creating your Color Palettes

Here was the UI:

Here are my new designs I plan to test for bright sunlight:

Paddle Mate: Getting Ready for Testers

Fabric metrics show meSelfie Band
had 22 commits to the master and
has been 100% crash free so far.

For usage metrics and crash reporting I use Fabric.io. Once I have an app to a point where I am ready to give it to a friend to test out I add Fabric.

Fabric let's you setup Beta tests (though I use Test Flight once I have an app that can pass App Store Review for Test Flight Betas), track app usage, and retrieve crash reports if you app crashes.

When I add Fabric I also add versioning to my app so I can track the builds that are in the wild with Testers or in the App Store. For that I use a script in the Build Phases.  My script is triggered off my number of commits to the branch I am working on. So a feature branch will have the feature in the version but an AppStore build will have just the number of commits (from the master branch, where I ship from).

Here is the one I use which I have a link to the original in my comments:

#

# Set the build number to the current git commit count.

# If we're using the Dev scheme, then we'll suffix the build

# number with the current branch name, to make collisions

# far less likely across feature branches.

# Based on: http://w3facility.info/question/how-do-i-force-xcode-to-rebuild-the-info-plist-file-in-my-project-every-time-i-build-the-project/

#

git=`sh /etc/profile; which git`

appBuild=`"$git" rev-list --all |wc -l`

if [ $CONFIGURATION = "Debug" ]; then

branchName=`"$git" rev-parse --abbrev-ref HEAD`

/usr/libexec/PlistBuddy -c "Set :CFBundleVersion $appBuild-$branchName" "${TARGET_BUILD_DIR}/${INFOPLIST_PATH}"

else

/usr/libexec/PlistBuddy -c "Set :CFBundleVersion $appBuild" "${TARGET_BUILD_DIR}/${INFOPLIST_PATH}"

fi

/usr/libexec/PlistBuddy -c "Save" "${TARGET_BUILD_DIR}/${INFOPLIST_PATH}"

echo "Updated ${TARGET_BUILD_DIR}/${INFOPLIST_PATH}"

Paddle Mate now has 

Paddle Mate: Mapping + Icon

I have added MapKit to Paddle Mate and can now trace my route. That has been pretty cool.

Tomorrow I will test pace, distance, and route updates during a workout. You can also now look at your route while Kayaking.

And I built a first pass for an app icon which i think looks much nicer on the phone than here :-)

Also just started to integrate Realm.io and will post on that more later. I am using Realm.io over CoreData since I have never bought into CoreData's overweight API. I like the lightness of the Realm.io API and I can get behind that. It appears to get out of the way and let me concentrate on the app instead of fighting with CoreData.

We will see in the next few days while I integrate Realm.io into the app to store sensor data (for submitting samples for post workout analysis) and to store workout data including map locations for post workout review.

Hackathon: Writing a Microsoft Band App In One Weekend - Update 5

Update 5: The Sunlight Strikes Back

Yesterday I start working on MapKit integration and a higher contrast Work View. Though I find the font and weight a little heavy, for bright sunlight I figured I should err on the side of overweight and work backwards.

Here is the last prototype UI I used yesterday:

Here are the new UIs I will try out next:

Map It with MapKit

I also began to add mapping to the app. I want to track distance covered and pace. So, since I am doing that I might as well put in a MapView to let you see where you are. I am undecided on how much I will build that out. A review of other fitness apps shows that most have a full screen MapView but I am unsure that is required for my intended purpose.

Perhaps for someone navigating a series of rivers or along the coast it might be handy to be able to zoom out. I would hope that someone was not using the app for navigation though; as that is not the intended purpose.

My goal with a map view is to see if I am tracking close to a given route I might want to repeat for comparison.

What do you think? Would a full screen MapView be useful?

The Last 20%

To productize this app for sale here are some of the features I think it needs. Let me know if you think I am wrong.

1. Main Page with list of previous workouts.
2. Export option to:
• Health App
• Strava
• Runkeeper
• Endomondo
• Facebook
• Twitter
• Mail Developer Session Samples (So I can continue to refine the algorithm)
3. View Previous Workout
4. Workout Mode
1. Basics working.
2. Missing MapKit for Distance, Pace

As well, I need to find a unifying design, color scheme, typography, and all the other details to make the app not look terrible.

Hackathon Result

I am calling this a decent start to a new app. Here are some of the wins from this weekend:

• The basic algorithm is 80%+ accurate on stroke detection.
• I have a platform to build out skills in algorithm design for a fitness apps.
• I can export data from the app for post workout analysis.
• I have a new excuse to hit the water more for some kayaking.

To those that read along, Thanks! It was nice to have feedback and see that people were interested in this topic.

If you kayak and have an iPhone, get in touch with me mark@newthistle.com.

If you want to see some of the products I have worked on or get on touch to collaborate on a project check out my company NewThistle LLC.

You can sign up to find out when I do another Hackathon weekend or launch an app at http://www.newthistle.com/contact/

Hackathon: Writing a Microsoft Band App In One Weekend - Update 4

Update 4: Hear My Heart Beat

Can you see the 20 strokes in here? Paddle Mate found 17.

It was a dark and storming night. Taylor Swift sang her heart out in downtown Seattle while I coded my heart (beat) out writing Swift in Sammamish.

But enough of that, onto the progress report. Last night I:

• Added Heart Rate Monitoring into the app.
• I said what the heck, let's grab that calorie count from the Microsoft Band.
• Then I lost my mind and decided to add a "Reset" into the app to make collecting data easier.

Oh ya, then I hooked the stroke counter. It was about time I added the feature I started this app for.

Heart Rate

I now collect the Heart Rate samples while I update the current Heart Rate label in the app. Once the workout is complete you can now view the Heart Rate in the in-app graph using the heart rate/stroke toggle icons along the right of the graph.

Calorie Counts

Just displaying the calories burnt during the workout, as reported by the Microsoft Band.

Reset

During data collection on Saturday morning I had to force close the app to clear it. I had to do this between each data collection.

We aren't animals here. Force closing apps? What will be next? Side loading apps you got from a friend off of 10x3.5" floppy disks which are really not floppy? No, we won't.

Behold the Holy Reset of Antioch!

Yes, the reset was that good.

Wet And Wild 2: Get Me Some Samples

With only one Sprint Pace sample I decided I needed more samples. Here are the results of today's collection with the Stroke Detector I worked on yesterday.

Pace	Strokes	Detected Strokes	Accuracy
Easy	30	27	83%
Sprint	10	10	100%
Easy	25	23	92%
Sprint	20	17	85%
Easy	50	47	94%
Mixed	120	96	80%
Easy	100	84	84%

Overall I am pretty happy with the new stroke detector. With an 80%+ accuracy I think that is pretty good for a one day of crash course Linear Regression, Moving Average, Classification, and Standard Deviation coding session.

My next phase of stroke detection I think will involve some machine learning to see how more accurate I can get the algorithm. At the moment I see I need to do a couple things based on the samples:

• Classify strokes and trailing strokes by pace (Slow, Medium, Fast) and then use that classification to adjust the stroke detection algorithm.
• Better rejection of garbage samples (non-strokes).

About Class Structure

You know that joke about MVC? Massive View Controller?

That's this app at the moment.

I know, I should hang my head in shame. But screw it, I will put making this an architecture marvel to knock your socks off on my "Backlog".

To be truthful it is not all a MVC. I have the following classes:

• PaddleCounter class to handle stroke analysis.
• Ring class to handle the ring buffers for moving average calculations for the PaddleCounter.

The rest of the logic at the moment is in the ViewController but will change once I extend the app.

Notes From Wet Tests

During testing on the lake I made some notes about the app:

• As we know,trying to read an iPhone in the sun sucks. I am going to need a high contrast UI with very large components if the user has a hope in hell of seeing the strokes, heart rate, etc.
• The waterproof plastic case I have is soft and as such touching one point on the screen may cause a different location to register the tap. This means that during a workout I will need to add a dialog to prompt a user "Do you really want to stop you workout?"