AI-Driven Muscle Recovery (Paperback)

Edité par Independently Published, 2024

ISBN 13 : 9798303764712

Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail

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Paperback. AI-Driven Muscle Recovery: The Power of Microtears and Rebuild by Nik Shah is the ultimate technical guide to understanding how artificial intelligence (AI) is revolutionizing the field of muscle recovery. This book delves deep into the scientifically backed process of muscle regeneration, focusing on the role of microtears and how AI technologies are enhancing muscle rebuilding at an unprecedented rate. Through AI-powered systems, biomechanical optimization, and data-driven recovery strategies, this book offers a roadmap for anyone seeking to accelerate recovery, enhance performance, and optimize overall muscle health.In AI-Driven Muscle Recovery, Nik Shah, a recognized leader in AI, biomechanics, and sports medicine, integrates advanced technological principles with actionable insights for professionals and fitness enthusiasts alike. This resource is designed for those looking to apply AI to support faster muscle recovery, with specific attention to the microtear process and how it contributes to muscle growth and strength.Key Highlights: AI-enhanced recovery: Explore how artificial intelligence algorithms analyze muscle damage and microtears at the cellular level, creating personalized recovery protocols to accelerate healing.Biomechanical optimization: Learn how AI-driven systems optimize muscle repair through precision modeling of muscle fibers, tendon regeneration, and ligament healing.Muscle microtears: Gain a deep understanding of the microtear process in muscles and how it contributes to muscle hypertrophy and recovery. This book highlights the critical role of these small injuries in muscle growth and strengthening.Data-driven recovery strategies: Implement data-backed recovery techniques using AI to monitor progress and adjust therapies based on individual muscle repair needs.AI applications in sports medicine: Learn how AI and machine learning are transforming sports medicine, offering solutions for athletes, trainers, and rehabilitation specialists.Advanced Insights: Nik Shah applies his technical expertise to show how AI is not just a tool for optimizing recovery but is a game-changer in creating personalized recovery plans. By leveraging machine learning algorithms, AI can predict recovery times, optimize nutrition plans, and even recommend exercise regimens for injury prevention and performance enhancement.Practical Applications: This book is indispensable for athletes, coaches, physiotherapists, and anyone interested in optimizing muscle recovery. Whether you're recovering from an injury or seeking to maximize muscle hypertrophy, AI-Driven Muscle Recovery provides a technical framework for using AI to push the boundaries of human performance. Nik Shah's insights into AI in sports medicine and muscle regeneration open new possibilities for faster, more efficient healing, and optimized muscle growth.About the Author: Nik Shah, based in Westford, Massachusetts, is a pioneer in the integration of AI and sports recovery technologies. With a background in finance, accounting, and social entrepreneurship from Northeastern University, along with a deep commitment to advancing sports science, he brings a unique interdisciplinary perspective to AI-driven muscle recovery. His expertise in AI systems and his experience with xAI Robotics place him at the forefront of the AI-enh Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9798303764712

Détails bibliographiques

Titre
AI-Driven Muscle Recovery (Paperback)
Auteur
Sean Shah
Éditeur
Independently Published
Année de publication
2024
État de l'article
new
Reliure
Paperback

CitiRetail

Stevenage, Royaume-Uni

Vendeur avec une évaluation de 5 étoiles

Vendeur AbeBooks depuis 29 juin 2022

Frais d'expédition de Royaume-Uni vers Etats-Unis

Article7 à 14 jours ouvrés7 à 60 jours ouvrés
Premier articleEUR 43,15EUR 43,15
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