A COMPREHENSIVE REVIEW OF AI (ARTIFICIAL INTELLIGENCE) BASED HEALTH MONITORING WEARABLES DEVICES

Abstract

AI-based wearable health monitoring devices are transforming healthcare by enabling continuous, noninvasive, and real-time monitoring of physiological parameters, such as heart rate, ECG, EEG, blood pressure, glucose levels, respiration, and body temperature. These devices integrate advanced biosensors, edge computing, and machine learning (ML) or deep learning (DL) algorithms to detect, predict, and manage chronic diseases effectively. Examples of such devices include Apple Watch, Fitbit Sense, Hexoskin Smart Shirt, Garmin Vivosmart, and BioSticker, which use biosensors to collect electro-physiological and electro-chemical signals. AI/ML algorithms analyze complex time-series and image-based data to provide personalized insights, early diagnosis, and continuous patient monitoring. Data preprocessing, secure wireless transmission, and energy-efficient solutions enhance the reliability and usability of these devices. Future research is expected to focus on advanced machine learning models, explainable AI (XAI), embedded and edge AI, multimodal data fusion, and reducing the dependency on clinical parameters to expand accessibility and performance. Ethical considerations, including data privacy, algorithmic transparency, and equitable access, are critical for the safe and responsible adoption of AI. Despite challenges such as sensor reliability, black-box models, high computational requirements, and cost limitations, AI-powered wearables hold immense potential for improving patient outcomes, supporting remote healthcare, and transforming traditional healthcare systems.

Key Points:

• Continuous, noninvasive monitoring of vital signs and chronic conditions.


• Integration of advanced biosensors with AI/ML and DL for predictive analytics.


• Real-world devices: Apple Watch, Fitbit Sense, Hexoskin Smart Shirt, Garmin Vivosmart, and BioSticker.


• Data preprocessing, secure wireless transmission, and energy-efficient solutions enhance the efficiency of the devices.


• Ethical considerations include privacy, transparency, and equitable access.

Future research should focus on explainable AI, edge computing, multimodal data fusion, and reducing dependence on clinical variables.