The application is an aggregator of information from all smart devices (smart clocks, smart scales, smart refrigerator and so on) for the purpose of collecting and analyzing these data. The neural network analyzes the obtained data and independently calculates the quantitative and qualitative characteristics of the person's physical activity. The application also includes a social network of people with healthy lifestyle
At this stage there are: - Working neural network prototype - UX designs - Potential customer - Development Team, our company: https://it-bulls.com
According to our surveys, our target audience is: - Men and women from 20 to 40 years, leading a healthy lifestyle, users of smart devices
Problem or Opportunity
Today, there are many fitness trackers. But according to our surveys - they do not meet the requirements of end users. Namely: - The need to manually maintain a training log, not automatically - Inaccurate display of information (a person can shake the phone in his hand, and the user will see that he went through 10 steps) - Lack of information display (limited set of metrics describing a person's physical condition) - Lack of analysis of information about the physical condition of a person, taking into account the individual characteristics of the organism - A bunch of separate applications to integrate with different smart devices
Solution (product or service)
The solution to the above problem is a neural network that recognizes all the physical activity of a person based on all the sensors that are in the smart devices.
Integration with smart devices will save the user from having to use different applications to integrate with different smart devices.
First of all, there are no direct analogues to our product. Because no other application: Analyzed information from smart scales and the results of physical activities together Traced the type of exercises automatically Traced quantity and quality of exercises Studied and adjusting to the user.
But nevertheless, there are a number of fitness applications that have a declarative similarity in functionality. These are applications such as:
Fitness application brand Myfitnesspal. https://www.myfitnesspal.com/ All applications can be found here https://www.myfitnesspal.com/apps One of the leading applications is the pocket coach of Endomondo https://www.endomondo.com/
Fitness app Strava https://www.strava.com/
Samsung Health http://www.samsung.com/en/apps/samsung-health/
Google Fit https://www.google.com/fit/
And also a list of other most popular fitness applications and fitness trackers: Active https://www.active.com/mobile/ ActiveX http://activexapp.com/ Sworkit https://sworkit.com/ Pear https://pearsports.com/ Lose it http://loseit.com/ Diet Bet https://www.dietbet.com/ Workout Trainer https://play.google.com/store/apps/details?id=com.skimble.workouts&hl=en FitBit Coach https://www.fitbit.com/home Fit Notes https://play.google.com/store/apps/details?id=com.github.jamesgay.fitnotes&hl=en
Advantages or differentiators
The key differences are the processing of information from all smart devices by a neural network. No one has done that yet. This approach provides the maximum individuality, accuracy and quality of the analyzed data.
There are several potential ways of earning: - Sale app to a company (such as Samsung, LG, Nike, Adidas). A successful example is the sale of MyFitnessPal to Under Armor for $ 475 million - Sale of a packaged SDK for new applications - Earnings on a running application from built-in purchases
Money will be spent on
- Salaries of programmers - Servers - Office rent
Offer for investor
The investor is offered equity participation, shares of the company. Details can be discussed during the meeting.