26 citations · 103 across the 33 of their papers we have counts for
37 papers
You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition
Siyu Yuan, He Zhang, Sizhen Bian +1
Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a complementary view inspired by…
EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
He Zhang, Siyu Yuan, Siyu Liu +2
Sensor-based human activity recognition (HAR) is fundamental to ubiquitous and wearable computing, yet existing foundation models are largely designed for cloud-scale deployment an…
KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
Mengxi Liu, Sizhen Bian, Vitor Fortes +5
Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy a…
Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
Sizhen Bian, Mengxi Liu, Lala Shakti Swarup Ray +7
Sensor-based Human Activity Recognition (HAR) underpins many ubiquitous and wearable computing applications, yet current models remain limited by scarce labels, sensor heterogeneit…
Calibration-Free Induced Magnetic Field Indoor and Outdoor Positioning via Data-Driven Modeling
Qiushi Guo, Matthias Tschoepe, Mengxi Liu +2
Induced magnetic field (IMF)-based localization offers a robust alternative to wave-based positioning technologies due to its resilience to non-line-of-sight conditions, environmen…
Bridging Generalization and Personalization in Human Activity Recognition via On-Device Few-Shot Learning
Pixi Kang, Julian Moosmann, Mengxi Liu +4
Human Activity Recognition (HAR) with different sensing modalities requires both strong generalization across diverse users and efficient personalization for individuals. However,…
Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
Sizhen Bian, Mengxi Liu, Paul Lukowicz
Passive body-area electrostatic field sensing, also referred to as human body capacitance (HBC), is an energy-efficient and non-intrusive sensing modality that exploits the human b…
TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey +2
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces Tini…
iBreath: Usage Of Breathing Gestures as Means of Interactions
Mengxi Liu, Daniel Geißler, Deepika Gurung +5
Breathing is a spontaneous but controllable body function that can be used for hands-free interaction. Our work introduces "iBreath", a novel system to detect breathing gestures si…
From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
Mengxi Liu, Lala Shakti Swarup Ray, Sizhen Bian +6
We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklac…
Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification
Sizhen Bian, Vitor Fortes Rey, Siyu Yuan +1
While human body capacitance () has been explored as a novel wearable motion sensing modality, its competence has never been quantitatively demonstrated compared to that of th…
Optimization of An Induced Magnetic Field-Based Positioning System
Sizhen Bian, Gerald Pirkl, Jingyuan Cheng +1
Using oscillating magnetic fields for indoor positioning is a robust way to resist dynamic environments. This work presents the hard- and software-related optimizations of an induc…
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
Mengxi Liu, Daniel Geißler, Sizhen Bian +2
Human activity recognition (HAR) ideally relies on data from wearable or environment-instrumented sensors sampled at regular intervals, enabling standard neural network models opti…
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field
Sizhen Bian, Vitor Fortes Rey, Siyu Yuan +1
The passive body-area electrostatic field has recently been aspiringly explored for wearable motion sensing, harnessing its two thrilling characteristics: full-body motion sensitiv…
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…
Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medical Diagnosis
Pietro Bonazzi, Yawei Li, Sizhen Bian +1
This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a…
Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware
Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis +3
This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework integrates a directly…