When I look at the hype around wearables, I see a lot of misdirected effort. Polar launched their first wearable heart rate monitor in 1982, and basically all modern wristbands are utilizing the same principles as that original wearable.

That might give you some insight into why tech-savvy athletes are considering wristbands “overpromised and under delivered” -- as a generation of wearable gadgets. The current set relies on the same heart-rate data as Polar has used in their products for almost 40 years.

Fifteen years ago I started my personal “quantified self” movement with a Polar device. After a couple of weeks I’d learned my sweet spot for running heart-rate, and I can’t really find the added value for me as a consumer when the Fitbit Flex — among others– surfaced in 2013. I already had several similar gadgets resting in peace in my gadget graveyard.

Graveyard of my gadgets breaks my bank

Graveyard of my gadgets breaks my bank

As a passionate gym trainer from the late 90s, I’ve been seeking optimization and simplification for almost 20 years. What is the very essence of training? When should I hit the gym again for maximum efficiency? How hard should I train for effectiveness? The current best practice in training science revolves around the Supercompensation theory.

Supercompensation in a nutshell

Supercompensation in a nutshell

In early 2000 I used Excel to analyze my exercises — only to realize that it’s overly time consuming and the manual analysis of weight, reps, and sets doesn’t help with timing supercompensation for my workouts. Since I couldn’t find any solutions on the market for optimizing based on a timing window with split routines (e.g. leg day, chest day, etc.), I started developing a new set of formulas to automatically interpret data, and predict the perfect supercompensation times for my next workout.

The patent for that system “METHOD AND SYSTEM FOR GAINING BALANCED HEALTH AND FITNESS REGIME” was filed in 2012, and the first application based on that implementation of the theory was launched in 2013 via Apple’s iOS. At the time, smart clothing systems were not mature enough, and so I took an application-only approach. The simple idea was to provide ideal supercompensation training times for the next workout, all based on targetable muscle groups — all while avoiding the complex biological science.

This Minimum Viable Product was intended to interpret data from future smart clothing, although currently the data input is done manually. Researchers from “Center of Sport” in Finland, the University of Jyväskylä, home to the Finnish Olympic research institute, welcomed our scientific approach. And the results have been delightful. Just in Finland RecoApp has over 10 000 users! As expected, however, the number one complaint common with all fitness apps is the manual entry of data. A problem that will be solved when smart clothing technology matures.

RecoApp uses simple traffic light planning for workouts

RecoApp uses simple traffic light planning for workouts

By using RecoApp I’ve managed to keep my performance levels continuously high. Even though I was using the exact same methodology prior to getting my app, the sheer simplicity has allowed for massive gains. My Super Squats from this spring are 10x350kg, only because I have now maxed out the machine. I’m more energized and have avoided any overtraining or undertraining symptoms that are very typical with even amateur gym exercisers. I still use RecoApp for monitoring every workout, and am finally able to aim for continuous supercompensation with next to minimal time investment.

RecoApp delivers an easy and efficient method for interpreting the data from your Smart Clothing

RecoApp delivers an easy and efficient method for interpreting the data from your Smart Clothing

So what is in store? When the RecoApp system mates with the latest smart clothing technology — such as muscle activity sensors like EMG — we can help manufacturers interpret this massive amount of data and display it to customers via simple color coding based on rigorous training science. What’s more, RecoApp can synthesize your various wearable monitors, or even add data from other device classes (e.g. sleep tracking from our current partners). There’s no need for the exerciser to input data or interpret data, and since the system is automated they’ll never have to pull out their phone while training.

Thanks to RecoApp we can finally shake the “overpromised and underdeveloped” cloud that has hung over every generation of wearables since the early 80s. We’ll be able to exceed the expectations of customers by offering hassle free data analysis with RecoApp from smart clothing technology. Thus analyzing, adapting, and predicting continuously optimized supercompensation times for muscular workouts. With the combination of these technologies, the average uninformed gym exerciser will have a greater understanding of their training methodology than serious self-experimenters like myself were able to pull off even 3 years ago.

 

Markus Mäntynen is the CEO and founder of RecoApp Oy and also Startup Coach & Project leader in Jyväskylä Business and Innovation Factory.