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?"