Athlerse Whitepaper
  • INTRODUCTION
    • Introducing Athlerse
    • Disclaimer
    • Our Vision
    • Target Market & Sectors
    • Existing Problems
    • Product Proposition
    • Why Athlerse?
    • Definitions
  • HOW DOES IT WORK?
    • Top-quality Workouts for Free
    • Get Rewarded for Your Training
    • Extend Fitness Functionality
    • Discover Full Web3 Potential
    • Social Interaction with Athletes
    • Go-to-Market Strategy
  • Technical Aspects of Athlerse
    • Technology Stack
    • Web3 Tech Aspects
    • Blockchain Infrastructure
    • Trackers Integration
  • Athlerse AI Initiatives
    • Athlerse AI Functions
    • Personal Workout Plans
    • Smart AI Workout Analysis
    • Posture Analysis and Correction
    • AI-Powered Predictive Analytics
    • Voice Guidance & Virtual Trainer
    • AI-Powered Nutrition
    • Technical Overview
    • Athlerse AI Summary
  • Tokenomics
    • Tokenomics: Introduction
    • Tokenomics and Valuation
    • GYM Coin
    • $ATHL Token
  • Business Model
    • Product Financials & Monetisation
  • Roadmap
    • Roadmap
  • TEAM & PARTNERS
    • Meet the Core Team
    • Incubators & Partners
  • Use Cases
    • Use Case 1: Fit Mum
    • Use Case 2: 18 Years Old Teenager
    • Use Case 3: Trainer
  • OFFICIAL LINKS
    • Athlerse Website
    • Athlerse for Coaches & Athletes
Powered by GitBook
On this page
  1. Athlerse AI Initiatives

AI-Powered Nutrition

A balanced diet is a critical component of any fitness journey. Recognizing its importance, Athlerse will offer AI-powered nutrition recommendations and meal planning to complement the workout features. This will provide personalized dietary advice based on each user's fitness goals, dietary preferences, allergies, and nutritional needs. The app will offer meal planning assistance, recommending a variety of meals and snacks that align with the user's dietary requirements and workout regimen. By considering factors like workout intensity, timing, and specific health goals, the AI system can suggest optimal nutrient intake for pre-workout energy, post-workout recovery, and overall health.

The technical implementation of this feature involves the use of machine learning algorithms that can analyze and learn from a user's dietary habits, workout intensity, and feedback on suggested meals. These algorithms are trained on a vast dataset consisting of various diets, meals, individual foods and their nutritional components, allowing the system to generate nutritionally balanced and diverse meal plans. The system will employ recommender algorithms to suggest meals based on user preferences and dietary needs, with reinforcement learning feedback loops to reflect on user’s preferences

PreviousVoice Guidance & Virtual TrainerNextTechnical Overview

Last updated 1 year ago