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Digital Twin Coaching for Physical Activities: A Survey.

Rogelio Gámez Díaz1, Qingtian Yu1, Yezhe Ding1

  • 1Multimedia Communications Research Laboratory, University of Ottawa, Ottawa, ON K1N6N5, Canada.

Sensors (Basel, Switzerland)
|October 24, 2020
PubMed
Summary

This survey explores Digital Twin Coaching, integrating machine learning and physical activity coaching. It defines the concept and outlines future research directions for enhanced health and sports performance.

Keywords:
artificial intelligencedeep learningdigital twinfitnessmachine learningobesityrehabilitationsmart coachingsports

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Area of Science:

  • Computer Science
  • Sports Science
  • Health Informatics

Background:

  • Digital Twin technology adoption is increasing, driven by machine learning advancements.
  • Rising life expectancy necessitates greater focus on physical activity and health management.
  • Sedentary lifestyles, exacerbated by events like quarantines, highlight the need for innovative health solutions.

Purpose of the Study:

  • To survey Digital Twin technology, specifically focusing on machine learning and coaching techniques.
  • To define Digital Twin Coaching and categorize existing research based on physical activity objectives.
  • To identify common characteristics and future research perspectives in Digital Twin Coaching.

Main Methods:

  • Literature review of Digital Twin technology, machine learning, and coaching techniques.
  • Categorization of studies based on physical activity goals.
  • Analysis of Digital Twin Coaching characteristics like interactivity, privacy, and security.

Main Results:

  • A definition and categorization of Digital Twin Coaching are established.
  • Key characteristics such as interactivity, privacy, and security in Digital Twin Coaching are identified.
  • Future research directions including multimodal interaction and standardization are detailed.

Conclusions:

  • Digital Twin Coaching presents a novel approach to personalized physical activity and health management.
  • The study provides a foundational understanding and roadmap for future research in this interdisciplinary field.
  • The proposed Digital Twin Ecosystem models can guide the development of advanced athlete tracking and coaching systems.