26 citations · 103 across the 33 of their papers we have counts for
8 papers · 1 filter
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface
Sizhen Bian, Pixi Kang, Julian Moosmann +4
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancem…
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network
Sizhen Bian, Elisa Donati, Michele Magno
Spiking neural networks (SNNs), a brain-inspired computing paradigm, are emerging for their inference performance, particularly in terms of energy efficiency and latency attributed…
Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic Sensing
Sizhen Bian, Rakita Strahinja, Philipp Schilk +5
Step-counting has been widely implemented in wrist-worn devices and is accepted by end users as a quantitative indicator of everyday exercise. However, existing counting approach (…
On-Device Training Empowered Transfer Learning For Human Activity Recognition
Pixi Kang, Julian Moosmann, Sizhen Bian +1
Human Activity Recognition (HAR) is an attractive topic to perceive human behavior and supplying assistive services. Besides the classical inertial unit and vision-based HAR method…
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition
Mengxi Liu, Daniel Geißler, Dominique Nshimyimana +3
In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activ…
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets
Mengxi Liu, Sizhen Bian, Bo Zhou +1
This work proposes an incremental learning (IL) framework for wearable sensor human activity recognition (HAR) that tackles two challenges simultaneously: catastrophic forgetting a…
CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics
Victor Luder, Sizhen Bian, Michele Magno
Cycling power measurement is an indispensable metric with profound implications for cyclists' performance and fitness levels. It empowers riders with real-time feedback, supports p…
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey
Sizhen Bian, Mengxi Liu, Bo Zhou +2
Due to the fact that roughly sixty percent of the human body is essentially composed of water, the human body is inherently a conductive object, being able to, firstly, form an inh…