TY - JOUR
T1 - Edge AI on Smartwatches for Atrial Fibrillation Detection: A Perspective on Real-Time Processing, Power Efficiency, and Clinical Integration
AU - Bruno Martins
AU - Lucas Pereira
AU - Renata Azevedo
AU - Pedro Costa
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2023
VL - 2
IS - 1
SP - 64
N2 - Atrial fibrillation (AFib) is a major and often undiagnosed risk factor for ischemic stroke, with paroxysmal episodes that frequently evade conventional intermittent monitoring. Wearable devices combining photoplethysmography (PPG) and single-lead ECG have enabled large-scale AFib screening, but many current systems rely on cloud-based processing, introducing latency, connectivity dependence, and privacy concerns. While clinical studies demonstrate promising detection performance, real-world deployment remains limited by the lack of fully continuous, autonomous operation. Edge artificial intelligence (AI), which enables on-device deep-learning inference directly on smartwatches, represents a key advancement toward real-time, scalable AFib detection. By eliminating reliance on cloud infrastructure, edge AI reduces latency, enhances privacy, and supports immediate alerts during transient arrhythmic events. However, practical implementation requires careful optimization of model efficiency, power consumption, and hardware constraints alongside clinical validation. Future progress will depend on multi-objective design strategies that integrate accuracy, latency, and energy efficiency, as well as collaboration among engineers, clinicians, and regulators. Addressing challenges such as alert fatigue, equitable access, and data governance will be essential. Ultimately, edge AI has the potential to transform AFib management from reactive diagnosis to continuous, preventive monitoring, functioning as an unobtrusive, always-available cardiac safeguard.
UR - https://cirpublications.com/u591867573
ER -