Fitness | Darian Johnson's Favorite Things

Fitness Gear Review: Rhone’s Silvertech No-Show Socks

Rhone’s no-show no-slip socks are worth every penny.

My Rating:

Rowing machine. Check

Deadlifts. Check

Sprints. Check

Burpees. Check

Pushing a sled. Double check

Simply put: these socks work. THEY. DO. NOT. SLIP.

I knew these were awesome within the first five minutes of working out in them. One of my usual warm-ups is a 3-4 minutes on the rowing machine. Without fail, my old no-show socks always slip off my heel. It’s an annoyance… which becomes a major hindrance when I’m putting in work on the weighted sleigh.

I went through my entire workout before realizing that I never had to stop to readjust my socks. They never slipped. Rhone has placed a small rubber (or maybe its silicone) band right above the heel that stops the socks from slipping.

On top of that, the socks are comfortable. They have some cushion (which helps on runs), but not so much bounce that my lifts are impacted. The socks also have silver woven into the threads – which supposedly help with smells.

In short, $14 may seem like a lot, but it’s totally worth it. I have four pairs, and plan to add more when their next sale comes around (where sometimes you can get 10-20% off, or a free sock with another purchase).

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”

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.

[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.

Click image to enlarge

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 Intake

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”

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.

[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.