Using Amazon Machine Learning to Predict the Best Time of Day for Exercise – Pt 1: Overview

A simple application that uses your Fitbit data to recommend the best time of day to workout.

Best Time of The Day Workout Recommendation[su_note note_color=”#d3d3d3″]Note: This is the first post about using Amazon Machine Learning to predict workout intensity. Part 2 will cover the design of the Machine Learning Model. The 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]

A few months ago, one of my former co-workers1 published a brilliant post on Linked about using machine learning to predict his mood. It got me thinking – with all the data at my fingertips, what could – or should – I attempt to predict?

I ultimately decided to focus my attention on fitness. Specifically, I wanted to determine the most optimal time to workout on a given day.

Why? Well, when it comes to fitness, we live in a world of paradoxes.

We have the best fitness information available, the most knowledgeable doctors, and affordable devices to track our health.

We have the best workout options: gyms in every neighborhood; gyms at our offices; online and DVD courses to support fitness at home.

We even have more flexibility in our schedules. Many people work from home at least once a week. Our gyms open earlier and close later. Our employers support and encourage an active lifestyle.

Yet despite this, people struggle to find time to work out. And when we do get to the gym, we are either rushed or distracted – so our workouts become less impactful to our overall health.


I built my app to help myself, and others, find the most optimal time of the day to workout. Are my workouts more efficient in the morning? Or do I get better results by working out at noon?

Each user is different, so I had to build the app so that each user’s prediction model was customized to their activities. This was done by building a custom model for each individual user of the application.

I’ll talk in detail about how I designed the machine learning model and how the app is architected in parts 2 and 3 of this topic. In the meantime, feel free to use the web based version of the app or the Alexa skill.

Switching to Playstation Vue: Cost Analysis

Switching to PlayStation Vue could result in $1200 in savings over the next two years.

19992670539_23769d7c20_z[1]I have to admit something: I haven’t been eating my own dog food – at least when it comes to disruptive technologies. My company preaches leveraging disruptors; as a consultant, I constantly encourage my clients to take advantage of the newest tech to reduce capital (and operating cost).

So, I’m ashamed to say that I haven’t practiced what I’ve preached – when it comes to communication and media costs. I have a contract with satellite company. I have a home phone with a local provider. Last week, I received a bill from my local telco for $112 – and that’s just for internet service and a home phone (which my family only uses for the alarm and to have in case of an emergency). Continue reading “Switching to Playstation Vue: Cost Analysis”