26 citations · 103 across the 33 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…