Adaptive Yoga Intervention System for Remote Workers: A Computer Vision and AI-Based Framework

Abstract

Shifting to remote work for most people because of COVID-19 has significantly reduced physical activity, caused many people to develop postural problems and mental health difficulties and added more stress. Many people now try online yoga when struggling with these problems, yet most traditional platforms are not flexible or do not offer personalized responses which hinders their success and lessens engagement. Therefore, this paper presents the AAYC, an intelligent system that helps remote workers increase their physical activity and health. AAYC uses technology to keep an eye on your posture as you work and immediately tells you when you need to move, using audio and visual prompts. It applies machine learning to shape the user’s yoga session based on their strength, body measurements, how often they exercise and details of their heart rate. To support users, the program employs gamification with things like dashboards to show progress, prizes given in the app and achieving continuous session count milestones. When cloud and edge computing are combined, you can enjoy high performance, total privacy, the ability to use a device without internet access and very little data transmission. This study describes the plan of the system, its control procedures and important points such as adaptive scheduling, use of voice commands and feedback from users. Compared to video-based yoga programs, testing showed greater improvements in user engagement, pose accuracy and how useful the classes were perceived by students. The new system is designed to solve the main weaknesses of earlier approaches with its adaptable, flexible and user-focused approach. Because of this, it introduces a new level of digital wellness and may be added to larger remote health and corporate wellness efforts meant to lessen added long-term health dangers from working mostly from home a remote jobs.