25 citations · 37 across the 16 of their papers we have counts for
14 papers · 1 filter
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…
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…
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces
Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson +4
Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and s…
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities
Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan +4
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs)…
Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables
Alessio Burrello, Francesco Carlucci, Giovanni Pollo +5
PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained fo…
A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers
Jiawei Liao, Oscar Toomey, Xiaying Wang +4
Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this pape…