Machine Learning-Driven Design and Embedded System for Smart Wearable Devices

Abstract

With advances in sensors, wireless communication, and artificial intelligence, smart wearable devices have been widely applied in health monitoring, motion analysis, and human-computer interaction, evolving toward lightweight, low-power, and personalized designs. Machine learning enables these devices to analyze sensor data, recognize behavior patterns, health states, and environmental changes, enhancing personalization and adaptability. Embedded systems provide the operational foundation, supporting cooperation between hardware and software for data processing and functional execution. Human-computer interaction bridges users and devices, offering intuitive information access and control through refined interfaces and diverse sensing technologies. This paper examines the key technologies, system design, and computational logic of smart wearable devices, highlighting the roles of machine learning, embedded systems, and human-computer interaction. In addition, current technical bottlenecks are summarized, and future development trends are discussed.