Wearable Technology and Edge AI Advancements

The field of wearable technology and edge AI is rapidly advancing, with a focus on developing low-power, energy-efficient, and scalable solutions for real-time applications. Recent developments have centered around improving the accuracy and reliability of wearable devices for health monitoring and authentication purposes. Innovations in hardware-software co-design, neural architecture search, and edge AI processing have enabled the creation of devices that can operate for extended periods while maintaining high performance. Notably, the use of photoplethysmography (PPG) signals and electrocardiogram (ECG) signals has shown promise for continuous authentication and health monitoring. The integration of AI processing on wearable devices has also improved, allowing for real-time analysis and decision-making. Overall, the field is moving towards more practical and deployable solutions for real-world applications. Noteworthy papers include:

  • Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices, which presents a novel wearable device for atrial fibrillation detection with high accuracy and low power consumption.
  • BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing, which introduces an advanced multimodal biosensing platform for wearable applications with improved energy efficiency and real-time analysis capabilities.

Sources

A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment

Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices

Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm

Know Me by My Pulse: Toward Practical Continuous Authentication on Wearable Devices via Wrist-Worn PPG

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing

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