Open AccessA Comprehensive Review of Machine Learning, and Deep Learning in Wearable IoT Devices
Author(s) -
Minh Long Hoang
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573937
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This research explores the integration of Artificial Intelligence (AI) in wearable devices, including smartwatches, fitness trackers, smart clothing, and smart eyewear. Machine Learning (ML) and Deep Learning (DL) play a crucial role in enhancing these devices, leveraging sophisticated algorithms within the Internet of Things (IoT) ecosystem. AI-powered wearables incorporate metrology and advanced computational techniques, with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) driving applications in activity recognition, health monitoring, and personalized recommendations. The work presents case studies, highlighting AI applications in smart devices, such as stress detection via Heart Rate Variability, personalized exercise guidance, muscular activity monitoring, and real-time image recognition. Real-world implementations will illustrate the practical deployment of AI in commercial wearable products. Additionally, the research will address privacy and data security challenges associated with AI-driven wearable technology, ensuring the safeguarding of user information.