39 citations · 69 across the 7 of their papers we have counts for
11 papers
Towards an Earpiece for Wearable and Dry Ultrasonic Vagus Nerve Stimulation
Sebastian Frey, Farouk Ghezraoui, Marc Nölke +3
Ultrasound (US)-based auricular vagus nerve stimulation (aVNS) is emerging as a versatile, noninvasive modality for neuromodulation - spanning clinical therapies (e.g., pain manage…
MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion
Aidan Bradshaw, Marco Giordano, David Rode +8
The 3D center of mass (CoM) is a primary quantity in the biomechanical analysis of sport, rehabilitation, and clinical movement, yet existing 3D pose tracking, mesh recovery, and m…
Sub-Millisecond Event-Based Eye Tracking on a Resource-Constrained Microcontroller
Marco Giordano, Pietro Bonazzi, Luca Benini +1
This paper presents a novel event-based eye-tracking system deployed on a resource-constrained microcontroller, addressing the challenges of real-time, low-latency, and low-power p…
PuLsE: Accurate and Robust Ultrasound-based Continuous Heart-Rate Monitoring on a Wrist-Worn IoT Device
Marco Giordano, Christoph Leitner, Christian Vogt +2
This work explores the feasibility of employing ultrasound (US) US technology in a wrist-worn IoT device for low-power, high-fidelity heart-rate (HR) extraction. US offers deep tis…
SepAl: Sepsis Alerts On Low Power Wearables With Digital Biomarkers and On-Device Tiny Machine Learning
Marco Giordano, Kanika Dheman, Michele Magno
Sepsis is a lethal syndrome of organ dysfunction that is triggered by an infection and claims 11 million lives per year globally. Prognostic algorithms based on deep learning have…
i-CardiAx: Wearable IoT-Driven System for Early Sepsis Detection Through Long-Term Vital Sign Monitoring
Kanika Dheman, Marco Giordano, Cyriac Thomas +2
Sepsis is a significant cause of early mortality, high healthcare costs, and disability-adjusted life years. Digital interventions like continuous cardiac monitoring can help detec…
Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices
Marco Giordano, Silvano Cortesi, Michele Crabolu +6
This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of dis…
Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices
Marco Giordano, Silvano Cortesi, Prodromos-Vasileios Mekikis +3
In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This…
Towards a Novel Ultrasound System Based on Low-Frequency Feature Extraction From a Fully-Printed Flexible Transducer
Marco Giordano, Kirill Keller, Francesco Greco +3
Ultrasound is a key technology in healthcare, and it is being explored for non-invasive, wearable, continuous monitoring of vital signs. However, its widespread adoption in this sc…
Towards Robust Velocity and Position Estimation of Opponents for Autonomous Racing Using Low-Power Radar
Andrea Ronco, Nicolas Baumann, Marco Giordano +1
This paper presents the design and development of an intelligent subsystem that includes a novel low-power radar sensor integrated into an autonomous racing perception pipeline to…
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
Julian Moosmann, Marco Giordano, Christian Vogt +1
This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on m…