Going from Skinny Fat to Fit: No go to Yoga

Swallowing my pride and starting Yoga was one of the best fitness decisions I’ve ever made.

Five years ago, I would have laughed out loud if someone asked me to attend a yoga class. I could not imagine any sane man willingly subjecting himself to something as BORING and MUNDANE as yoga. Fast forward to today: practicing yoga is one of the highlights of my fitness routine. I love my weekly class; I always leave feeling exhausted, energized, and excited for the next opportunity to practice again.

When I started my fitness journey, I was primarily focused on building strength and stamina. The progress was slow and steady. I gained strength and felt healthier – plus, moving big, heavy things was a great stress reliever. Unfortunately, my flexibility began to suffer; my upper back and neck always felt tight. So, on the recommendation of my trainer, I started going to a yoga class. Continue reading “Going from Skinny Fat to Fit: No go to Yoga”

Getting the Most out of my Amazon Echo: The Roadmap

I’ve got an awesome smart device in the Amazon Echo. Now I need to use it.

Me with my Amazon Echo (and trophy from Hackster)
Me with my Amazon Echo (and trophy from Hackster)

Two weeks ago, I received an Amazon Echo as part of the prize for Hackster’s Internet of Voice challenge (2nd Place1). I checked the mail every day for its arrival, thinking of all the cool things I could and would do with it.

Fast forward to today, and I’m using the Echo daily… but not at its full potential. I use it for timers, connecting my phone via Bluetooth to play music, and to play Jeopardy. That’s not a diss: with four kids, timers are a necessity. Plus, I love Jeopardy. Still, with a device this powerful, I could be doing more. So, I’m putting together a plan to better use the device as a smart home assistant. Here, at a high level, is my roadmap. Continue reading “Getting the Most out of my Amazon Echo: The Roadmap”

Building a Magic Mirror using Alexa, AWS, and a Raspberry Pi

Mystic_Miror_Logo_NewSome people spend their vacations traveling, or relaxing, or visiting family. I spent my two weeks off building an Alexa enabled, Raspberry Pi device for Hackster’s Internet of Voice challenge.

But, to paraphrase Madonna: “Don’t Cry for Me, Internet.” I really enjoyed those two plus weeks of coding. I learned a ton about AWS IoT and MQTT (and re-enforced some “non-sexy” skills – like security and IAM).

And the device that I decided to build…. a magic mirror. Why a magic mirror? Well, I am the guy that:

  • Never checks for delays in his work commute until he is stuck in a four-lane accident
  • Forgets his umbrella when the forecast calls for afternoon showers
  • Doesn’t find out about a major news event unless the story breaks on ESPN
  • Always forgets to pull my trash bins to the curb on garbage pick-up day

In short, my morning routine is a mess (#firstworldproblems). An Amazon Echo (or a phone, for that matter) would resolve most of those problems. Unfortunately, I never seem to have my phone with me as I’m getting ready in the morning (it’s usually charging). And I’m usually not asking Alexa for these details (I don’t have an Alexa device in my bathroom).

60% of my morning routine is centered in and around the bathroom or bedroom, so I decided to build an Alexa skill and Alexa Voice Service-enabled magic mirror – which I’ve titled the Mystic Mirror.

Continue reading “Building a Magic Mirror using Alexa, AWS, and a Raspberry Pi”

Using Amazon Machine Learning to Predict the Best Time of Day for Exercise – Pt 3: Automating the Model with Alexa

Integration with Alexa allows a user to obtain a workout recommendation (and create a machine learning model) all by voice command.

002[su_note note_color=”#d3d3d3″]Note: This is the third post about using Amazon Machine Learning to predict workout intensity. Check out Part 1 (Overview) and Part 2 (Building the Machine Learning Model) for background. A working model is available via web and Alexa. Code can be found/downloaded from my Hackster site.[/su_note]

After I was able to build a working model, I needed to come up a way to automate the model. I originally planned to allow access through my website, but decided to use Alexa in addition to the website link.

Note: The process of creating an Alexa skill isn’t too complicated (if you have experience building lambda functions). That being said, I suggest you start by building a sample skill – such as the Fact Skill example. Also, be sure to read and follow the certification requirements.

Alexa, AWS, and the exposed Fitbit APIs provided a mechanism to build a model and return results for a specific user – all initiated by voice.

Step 1 – Linking the user’s Fitbit account to the skill

A user has to link his/her Fitbit account to the skill before s/he can (a) build a specific machine learning model based on their history and (b) get a workout recommendation. Step 1 covers the logic for this functionality.

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Continue reading “Using Amazon Machine Learning to Predict the Best Time of Day for Exercise – Pt 3: Automating the Model with Alexa”

Using Amazon Machine Learning to Predict the Best Time of Day for Exercise – Pt 2: The Learning Model

Amazon Machine Learning, leveraging activity data from a your Fitbit, can be used to predict workout intensity.

MachineLearning_VideoThumbnail[1][su_note note_color=”#d3d3d3″]Note: This is the second post about using Amazon Machine Learning to predict workout intensity. Check out Part 1 (Overview) for background. A third post will cover the Lambda and Alexa code used to automate the model. A working model is available via web and Alexa. Code can be found/downloaded from my Hackster site.[/su_note]

When creating my prediction model, I first had to define workout effectiveness. Was it measured by the total number of minutes that I worked out, or my average heart rate? Should I consider my peak heart rate, or number of calories burned.

After doing some research, I decided that workout intensity would be best captured my the number of “active minutes” for each workout. From the Fitbit website:

All Fitbit trackers calculate active minutes using metabolic equivalents (METs). METs help measure the energy expenditure of various activities. Because they do so in a comparable way among persons of different weights, METs are widely used as indicators for exercise intensity. For example, a MET of 1 indicates a body at rest. Fitbit trackers estimate your MET value in any given minute by calculating the intensity of your activity.

From there, I needed to determine what inputs impacted workout intensity. After more reading, I landed on the following as inputs to a successful workout.

  • Sleep (or lack of sleep)
  • Previous Day’s activities
  • Time of Day
  • Stress
  • Food Intake60700774[1]

My Fitbit tracks sleep and the amount of total activity of a given day (measured with the count of total active calories burned in a day). I can also get the start time of each workout and the resultant active minutes in that workout.

Stress, being subjective, is harder to measure. I originally used the number of meetings in my outlook calendar to determine my level of stress, but found that metric to be inaccurate (at least, without additional research/tuning).

Continue reading “Using Amazon Machine Learning to Predict the Best Time of Day for Exercise – Pt 2: The Learning Model”