Smartwatch-Assisted Exercise Prescription: Utilizing Machine Learning Algorithms for Personalized Workout Recommendations and Monitoring: A review

Abstract

This review paper examines the intersection of wearable technology, machine learning algorithms, and exercise prescription, focusing on the utilization of smartwatches to monitor physiological data during workouts. With the proliferation of smartwatches equipped with sensors capable of capturing various biometric parameters, alongside the advancements in machine learning, personalized exercise recommendations have become increasingly feasible. Through a synthesis of existing literature and analysis of recent developments, this paper explores the potential of integrating wearable technology and artificial intelligence to optimize exercise routines tailored to individual needs and goals. Key topics covered include the types of sensors found in smartwatches, machine learning algorithms used for exercise prescription, practical applications, challenges, and future directions. By providing insights into the current landscape and emerging trends, this review aims to inform researchers, practitioners, and policymakers on the opportunities and challenges in leveraging wearable technology and machine learning for personalized fitness monitoring and exercise prescription.