Digital Twin Coaching for Physical Activities: A Survey

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.