119 citations · 286 across the 27 of their papers we have counts for
29 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…
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…
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…
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…
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
Jonathan Dan, Una Pale, Alireza Amirshahi +9
The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-ter…
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology
Xiaying Wang, Lan Mei, Victor Kartsch +2
Brain-machine interfaces (BMIs) have emerged as a transformative force in assistive technologies, empowering individuals with motor impairments by enabling device control and facil…
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
Thorir Mar Ingolfsson, Upasana Chakraborty, Xiaying Wang +6
Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessi…
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices
Sizhen Bian, Xiaying Wang, Tommaso Polonelli +1
Automatic gym activity recognition on energy- and resource-constrained wearable devices removes the human-interaction requirement during intense gym sessions - like soft-touch tapp…
Nonlinear and Machine Learning Analyses on High-Density EEG data of Math Experts and Novices
Hanna Poikonen, Tomasz Zaluska, Xiaying Wang +2
Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically vali…
An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface
Jiawei Liao, Lars Widmer, Xiaying Wang +5
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation. High-accuracy and low-power algorithms are required to achieve implantable BMI sys…
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
Thorir Mar Ingolfsson, Mark Vero, Xiaying Wang +3
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to h…
Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications
Xiaying Wang, Fabian Geiger, Vlad Niculescu +2
Tactile sensing is a crucial perception mode for robots and human amputees in need of controlling a prosthetic device. Today robotic and prosthetic systems are still missing the im…
MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection
Xiaying Wang, Michael Hersche, Michele Magno +1
A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays a vital r…
Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces
Xiaying Wang, Lukas Cavigelli, Tibor Schneider +1
Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the cla…
SmartHand: Towards Embedded Smart Hands for Prosthetic and Robotic Applications
Xiaying Wang, Fabian Geiger, Vlad Niculescu +2
The sophisticated sense of touch of the human hand significantly contributes to our ability to safely, efficiently, and dexterously manipulate arbitrary objects in our environment.…
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices
Thorir Mar Ingolfsson, Andrea Cossettini, Xiaying Wang +5
We present the implementation of seizure detection algorithms based on a minimal number of EEG channels on a parallel ultra-low-power embedded platform. The analyses are based on t…
ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network
Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche +3
Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power fo…
Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces
Xiaying Wang, Tibor Schneider, Michael Hersche +2
With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution wh…
EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang +3
In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on electroencephalography(EEG). While…