most citedAn Ultra-Low Power Wearable BMI System with Continual Learning Capabilities

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2024

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…

eess.SP20245 cited

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)…

cs.RO20241 cited

GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones

Hanna Müller, Victor Kartsch, Luca Benini

The evolution of AI and digital signal processing technologies, combined with affordable energy-efficient processors, has propelled the development of both hardware and software fo…

eess.SY2023

A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring

Sebastian Frey, Victor Kartsch, Christoph Leitner +4

Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, it…

cs.RO20231 cited

Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones

Lorenzo Lamberti, Luca Bompani, Victor Javier Kartsch +3

Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions…