5 papers
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Francesco Carlucci, Giovanni Pollo, Xiaying Wang +6
Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is…
NanoHydra: Energy-Efficient Time-Series Classification at the Edge
Cristian Cioflan, Jose Fonseca, Xiaying Wang +1
Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user privacy and offer real-time predictions. Re…
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Alexandru Dimofte, Glenn Anta Bucagu, Thorir Mar Ingolfsson +4
Electroencephalograph (EEG) is a crucial tool for studying brain activity. Recently, self-supervised learning methods leveraging large unlabeled datasets have emerged as a potentia…
FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model
Anna Tegon, Thorir Mar Ingolfsson, Xiaying Wang +2
Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applications spanning hospital diagnost…
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
Luca Benfenati, Sofia Belloni, Alessio Burrello +6
Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show p…