Bike LED Vest Revisited

With Swift 3,  watchOS3, and iOS 10 released, it was time to migrate the code  from Swift 2.1 to Swift 3.0 using Xcode 8.0.  I still have use for my Myo armband but wanted to explore using Apple’s CoreMotion and HealthKit SDKs. The conversion tool in Xcode did a great job and most of it was dealing with optional chaining that required my attention. Once completed, the existing application worked as before. Although the bluetooth code migrated ok, I was left with a handful of deprecations warnings in iOS 10.0 that will need clean up so I can keep current on iOS releases.  For now, I wanted to explore how to use the iWatch Series 1 to achieve the following goals:

  • Capture Heart Rate and include with geolocation data in the  iPhone app
  • Detect and send turn signals to LED Vest
  • Display Temperature and Battery Voltage with alarm set points
  • Learn more about the Swift language, the various SDKs

Watch interface

Hats off to UIX designers. This does not come easy to me. I ended up purchasing Graphics (was iDraw) from Autodesk as it was affordable and comes with a rich feature set.  A nice bonus is that this tool can generate can generate Core Graphics “copy as” code.  Alas, my first pass works and leveraged the context menus to save on screen real estate.

bike-iwatch

 

 

 

 

Context Menu

The context menu used canned icons. The start/stop implement the obvious functionality.  The Speak functionality allows me to either send one of three messages or speak into the watch and send the resulting text to display on my LED Vest. The user interaction side was rather easy to implement using the presentTextInputController method of the WKInterfaceController class.

 

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Observations

Extensions

Swift extensions are your friend.  One can extend classes without access to source and add additional behaviour.  For example, to quickly implement alarm functionality, WKIntefaceLabel was extended to allow a refresh a label.

HealtKit

Healthkit is a pain to get going using just Apple’s docs.  Fortunately, there are lots of tutorials out there to help get things going.  I would have liked to have more detailed information so one can track heart rate variability (HRV).  These folks are working on some cool stuff mashing technologies using HRV. It seems we are left with BPM samples at around 5 second intervals. I get why, given that there could be other apps wanting access to this information and sub second resolution in a device that was meant to tell time at first would overwhelm watchOS. I suspect one day this should be available.

Core Motion vs. Myo

I found Myo integration to detect hand signals easier to implement. It was more intuitive. On the Myo, the “home” position would be cached so relative changes in movement could be computed. e.g.  right hand turn gesture using pitch and left hand turns using yaw and roll. It works quite well.

I could not get this to function using that same approach with consistent results on the iWatch.  I ended up working with just gravity component for hand signals position. e.g. right turn detection is using the X component of the accelerometer and the Y component for the left hand turn. Initially, I used CMDeviceMotion‘s CMAttitude  component and used multiply(byInverseOf: x) method of CMAttitude to get relative changes in motion.  It worked for the right hand turn but was inconsistent for the left hand turn.

The gravity only component with a range threshold range works ok for now. I will investigate the CMAttitude further and will look at integrating (summing changes along X,Y, Z over small sample windows to detect hand signals.

 

 

Bike LEDVest

I’ve been tossing this project in my head for a few years. I signed up when the Myo Armband came out on kickstarter and figured I could make use of it one day.   When I purchased the the Apple Watch, then that got the wheels in motion to build an LEDVest.

Some of the goals I wanted to achieve included the following:

  • Learn the iOS development (Swift language)
  • Drill down on Bluetooth LE development
  • Persist information on iCloud and retrieve from different devices
  • Create something useful and provides context based information to others while riding my bicycle at night
  • Explore iOS HealthKit and MapKit

Screen Shot 2016-02-06 at 5.26.43 PM

 

Prototype

My wife did all the sewing. The LED’s are so bright that the iPhone camera does not do it justice inside. Many people commented from motorists, pedestrians, and cycles on how cool this vest was.

It took a lot of effort but it was a nice diversion from the day job. Learning a new programming language, organizing the code so that the appropriate level of abstractions exist to easily add new features, creating an application level protocol to control the LEDVest, and designing and building simple hardware bumped up the fun factor.

Using my Apple Watch, I can speak text to display and I send it to the LEDVest to display. If I am annoyed at a stop light, I tend to keep it safe. e.g. “Smog sucks”.  So far the software periodically displays the temperature from the hardware, along with the WTI price and Canadian currency via the yahoo finance API. If I loose connectivity to the iPhone, the arduino portion fails-safe and displays the stop symbol and posts the temperature every 30 seconds.

I’ll talk about the implementation details later.

Munging my “Smart” Home Data

Context

For some of you who live in older homes, the feeling of too cold in the winter and too hot during the summer comes with the package. Last year, I thought of getting spray foam installed and figured now is the time to start analyzing the some of the data I’ve been collecting over the past three years. I told the installer that I had three years of data and would do a before and after test to see what the spray foam did in terms of performance. He stated he would purchase the data analysis. Needless to say one could just look at the heating bill to see if there is difference.  With variance in unit  costs of fuel, admin, etc., I did not want to bother normalizing that info. The geek in me wants to explore data mining and inference. So off I go to explore  Linear discriminant analysis and random forests.

For this exercise, I had three in-home temperature points, one outside, and several power related measurements. So far I had close to 3 million data points. I searched the web for an open source toolset that could help me with data analytics and decent plotting capabilities. Given I used the R programming language a few months back and liked the graphing capabilities in the ggplot2 package, it became my tool of choice. Note that Python is making in-roads in the data analysis space and for now, I want to remain focused on data analysis so R it is.

Rasberry Pi + Camera

Image

The idea of setting up a surveillance system has been on the to-do list for a long time. Given there have been a few break and enters in our neighbourhood for petty stuff and the last straw was a break-in in my vehicle, I decided to set one one up.  Why make it easy and purchase a ready to go camera like a foscam? It is would not be as exciting. After some searching, I ended up with purchasing the following from Newark Element14:

  • 2x -RASPBERRY-PI-RASPBERRY-PI/8GB-USD-MODEL B – 8GB SDCard W/ NOOBS PRE-INSTALLED
  • 2x – ADAFRUIT INDUSTRIES-1012-USB WIFI MODULE, 802.11b/g/n, RASPBERRY PI & BEAGLEBONE
  • 2x – RASPBERRY-PI-RPI CAMERA BOARD-ADD-ON BRD, CAMERA MODULE, 2RASPBERRY PI
  • 1x – RASPBERRY-PI-RPI NOIR CAMERA BOARD-CAMERA BOARD, BCM2835 RASPBERRY PI

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UDOO with SSD

I got around to set up the 120 GB SSD on the quad. I ended up making my own power cable with header wires to connect to +5 and Ground pins. I could not find a power connector that fit it J12 on the board at the local stores and didn’t want to order a small part online and pay for shipping.  What I have works.

The process was rather painless at first. I followed the procedure for creating a bootable image via OSX as described at the Udoo website. I then booted the board with the newly imaged SD card with the SSD connected to the board. I then installed gparted via

then ran

to partition the SSD and also create another partition on the SD card for backing up stuff since I had about 23 GB extra on the card to use.

ssd
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A Distraction – UDOO Quad

I received my UDOO Quad today. I was not expecting to dust off my faithful home energy system I wrote a couple of years ago. It has been running well yet I feel guilty of having a home computer running 24×7 to act as my SCADA host. The UDOO is the board that will blend both the Arduino and Linux in a nice board and allow Solid State Drive to hold all the data. On its own without a SSD it consumes around 3.7 watts in-standby.

I currently have about three years worth of energy and temperature data that I also don’t want to lose and it has to be migrated as well. I’m hoping to analyze it using the R-Language one day.  I’m hoping that it should be a relatively easy port as everything is cross platform.

UDOO Quad

 

 

GNU Octave OS X

Why GNU Octave

Learning image processing using C++ is not practical for a newbie like myself as it is not conducive to trial and error. Besides, I would like create a model and explore it in an iterative fashion before I code it in C/C++/ObjectiveC.

I opted to install GNU Octave on the Mac Mini since all my dev is on that platform.  I did install it on Windows 7 a while back for an earlier project so I know what it can do. I also wanted a tool that was almost 100% compatible with MatLab code.

Installing GNU Octave

My dev box consists of a Mac Mini with 2.3 GHz Intel Core i7, 16GB RAM running OS X version 10.8.3 (Mountain Lion). There seems to be a lot of issues with installing Octave in OS X based on what I see on the web. I went down the MacPorts path since I used it for some other installation.
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Spring Cleaning-Flow Meter

I read yet another book on iOS development and got the creative juices flowing with lots of ideas for some image processing.  That said, I’m starting to feel like a hoarder of electronic parts and opted to put a couple SeedStudio Water Flow Sensors to some use.  Besides someone asked me to describe how to use them in simple terms. So this side trip’s goal is put something together to measure the flow from kitchen faucets. The functional requirements include the following:

  • Hot water line measurement
  • Cold water line measurement
  • Current flow rate in L/sec
  • Running volume for the day in L
  • Max flow Duration for the day in seconds
  • Min flow Duration for the day in seconds
  • Average Duration for the day in seconds
  • Total flow duration for the day in seconds 
  • Integration with my existing M2M Mango instation over Modbus

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Back to Room One

Time to explore something outside my comfort zone. Image Processing.  I’ve gone through a few geek books during hard to find spare time. There are so many apps in the iOS world that I needed to dig a little deeper to get a better understanding what is under the hood.  iOS Programming: The Big Nerd Ranch Guide and Objective-C Programming: The Big Nerd Ranch Guide are good introductions. If you know C/C++ the Objective C book can be read rather quickly. I liked going through the exercises in the iOS programming book (well kindle version) to force me to navigate through the xcode/iOS documentation.  My real motivation is to do some image processing and opted to read the OpenCV 2 Computer Vision Application Programming Cookbook.

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Power Measurement Revisited

Spot Measurement

I finally got around to tinker with things again and opted to digress from the zigbee standalone mote-like development to revisit the power measurement.

I have three types of sensors to measure currents lying around and decided that given I already wrote the C++ classes and modbus integration for total home energy consumption, I could easily create a spot measurement modbus slave device to monitor specific loads.  Note that all my arduinos+zigbee are modbus slave devices and only the mote-like devices shall use a different protocol.

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