John KingWritten by | IoT & Big Data

Jillian Michaels is one of the leading health and wellness experts in the world. She is a television personality, entrepreneur, life coach, nutrition and wellness consultant, motivator, and a New York Times best-selling author. Jillian is a multi-millionaire personal trainer who is best known for her appearance on different television shows such as Losing It with Jillian, The Doctors, and The Biggest Loser.

Michaels is an expert who truly knows her field. Like Jillian, the best of the best know their field better than their peers and colleagues. They do this by immersing themselves in their industry, by going deeper than others are willing to go. They read libraries of books and reports, they talk to as many industry veterans as are willing, they understand the history of their industry, and they pay attention to the present trends of their trade. In essence, they immerse themselves in the data of their industry. They take in as much data as they can find, and process it. The most successful people in any industry understand that data and are able to see the future through it. Whether Warren Buffet in finance, Oprah Winfrey in media and entertainment, Tony Robins in behavioral psychology, or Jillian Michaels in personal fitness, industry leaders see what is coming and act faster and more creatively than others. They observe the data and compute it into action. According to Marketdata Enterprises, a market research firm that specializes in tracking niche industries, Americans spend north of $60 billion annually on fitness and weight loss. With CNN Money putting the median salary of a personal trainer at $56,000, with top pay of $128,000 (in 2012), it’s likely that a significant portion of that $60B goes to paying professionals and experts to assist us in our attempts to beef up or slim down.

The hourly cost of a personal trainer can vary from around $50 an hour, to upwards of hundreds per hour. In Jillian Michael’s case, you’d probably need to pay her thousands to work with you. We pay experts because they know what they’re doing. They have spent more time understanding the data of their industry than anyone else and they can process that data into actionable insights, which produce results. Experts are paid at the highest levels in their field because they understand and apply the knowledge (data) they have acquired in the most effective and efficient methods possible. We pay them to utilize their knowledge, incorporate the data we create, and process that into actionable insights that will make us better and produce the results we desire.

Like human intelligence, gathering and analyzing data to understand the best path forward is a key element of machine learning today. And like human intelligence, through data, machine learning can find the strongest indicators of causality.

With a background in biomedical engineering, Peter Li, Founder and CEO of Atlas Wearables, was exposed to many different startup ventures within fitness, healthcare, motivation during his time at Johns Hopkins as he earned his bachelors, then masters degrees. Atlas Wearables’ co-founder, Mike Kasparian, was working at Philips Healthcare while Peter was researching at Johns Hopkins.

It was through those experiences that they were exposed to the core problem of advanced machine learning for attracting 3D motion and how it could be applied to the fitness world.

At the time, Peter and a friend had started a program called the Sound Body Challenge. It was a three month program, very similar to other motivational challenges that are commonplace today: an individual would come in once a week, maybe on a Saturday, and a personal trainer would take them through a standard set of exercises and routines, grade their form, and give them feedback to improve.

What Peter and his team began to notice was those individuals who could compel themselves to come in every Saturday would be much more motivated to have better outcomes.

Through gauging intrinsic motivation (self-motivation), extrinsic motivation (a prize or reward for completion), and social responsibility present in fitness teams, iterative testing and observation demonstrated the vast potential to implement intrinsic motivation in a digitized workout regimen.

After their initial experience with the Sound Body Challenge, Peter got on the phone with Michael and through a series of Google Docs exchanges, they launched Atlas Wearables.

Peter, Michael, and their team started with an idea to help motivate people in the same way a personal trainer or fitness challenge program would, and they built an augmented personal trainer for your wrist. Now in an advanced iterated version, the Atlas Wristband2 is the first workout tracker that learns to track your form for various exercises. It can automatically recognize most exercises and even help you target Training Zones optimized to burn fat, tone, or bulk. It’s a waterproof device worn on your wrist, with a smartphone app and online portal to sync your personal data. The technology that sets Atlas Wearables apart from other wearable technology is the machine learning tracking capability. Essentially, you’re able to put this device on, do jumping jacks or any of their 100+ exercises, and Atlas will know that you’re doing that specific exercise. So pushups, triangle pushups, military pushups, Atlas can tell the difference between even the most incrementally differentiated movements.The Personal Trainer That Fits On Your Wrist

Atlas uses motion sensors on the wristband: a three-axis accelerometer, and a three-axis gyroscope. If you imagine a paint dot on your wrist moving around in 3D space, Atlas tracks that path, and each motion has a definitive fingerprint that they’re able to identify. It’s similar to what makes Siri run, but instead of looking at voice, Atlas is looking at a completely different dataset-motion. How does Atlas differentiate between movements? Machine learning. The problem other wearables have (those not utilizing machine learning) is in the similarity of movements. A pushup and power clean look nothing alike, so they have no issue. But what about a normal pushup versus a triangle pushup? Visually, as a human, they look very similar with the slightest variation. Atlas Wearable picks up on those variations. So Atlas has applied machine learning to elevate precision beyond that of other wearables. But what are the motivation factors built into Atlas technology, and how might that look to the regular user?

Intrinsic motivation is the core of their technology. Atlas is focused on augmenting the real-world experience of a personal trainer and client–movement + motivation.

Like a trainer, Atlas will keep track of different types of goals and metrics for you. It’s sort of like a GPS; it will provide a roadmap to where you want to go, even if you want to drive somewhere you’ve never been. You just put your GPS on and you don’t even think twice. By looking at the Atlas wearable model, it’s clear the Atlas team have dared to do things differently. Pedometer technology has made a strong name and case for itself in the fitness market, especially alongside the rise of wearable step-counting fitness brands like Fitbit.

By looking at the Atlas wearable dared to do things differently. Pedometer technology has made a strong name and case for itself in the fitness market, especially alongside the rise of wearable step-counting fitness brands like Fitbit.

Right now, the market is beginning to see a trend towards consumers who have used a pedometer for a while and are now beginning to look for more, as they are realizing that there may be more to their activity than just the step count. Atlas looks ahead to a fitness horizon where a wearable is just as informative to the user as that user is to the wearable.

In the constantly evolving intersection of fitness and technology, developers are often faced with expansive problems and no one clear solution. The success of Atlas is attributable to its unique design, both inside and out. With its “Tetris-like” look, the interactive elements present in its user experience, and the presence of high level machine learning elements to produce a truly unique experience, the Atlas wristband truly is one-of-a-kind. As the needs and wants of consumers change, Peter foresees many opportunities to expand into solution-oriented tweaks.

Atlas is already collecting a huge amount of data to best optimize consumer experience, and the wearable’s loyal community embraces plenty of opportunities to customize their wearable to the nth degree. Peter tells Cognitive Times that the data collected by the Atlas wristband is “only the surface” of the potential for future expansion.

As is the case with machine learning technology, the development of Atlas’ technology is iterative. They start with regular people (who represent and provide data), and move up from there. Although the product might be great for professional athletes, Atlas would rather build it for the masses.

Anyone concerned with fitness would love to work with the world’s top fitness experts. However, very few of us can afford to. Yet, that may be changing as we move toward machine learning that can augment what has historically been relegated solely to the realm of human expertise.

At the end of the day, Peter and his team want to be able to have the expertise an industry veteran like Jillian Michaels offers. They want to be able to capture her expertise and apply it to your unique goals, movements, and motivations. By leveraging advanced technologies and machine learning, they’re well on their way to building it onto your wrist.

To pick up an Atlas wristband, check out Atlas Wearables on Amazon, at Dick’s Sporting Goods, Luke’s Locker in the Austin area, or at www.atlaswearables. com.


Last modified: October 26, 2017