SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Human Activity Recognition (HAR) with wearable sensors is essential for
applications in healthcare, fitness, and human-computer interaction.
Bio-impedance sensing offers unique advantages for fine-grained motion capture
but remains underutilized due to the scarcity of labeled data. We introduce
SImpHAR, a novel framework addressing this limitation through two core
contributions. First, we propose a simulation pipeline that generates realistic
bio-impedance signals from 3D human meshes using shortest-path estimation,
soft-body physics, and text-to-motion generation serving as a digital twin for
data augmentation. Second, we design a two-stage training strategy with
decoupled approach that enables broader activity coverage without requiring
label-aligned synthetic data. We evaluate SImpHAR on our collected ImpAct
dataset and two public benchmarks, showing consistent improvements over
state-of-the-art methods, with gains of up to 22.3% and 21.8%, in terms of
accuracy and macro F1 score, respectively. Our results highlight the promise of
simulation-driven augmentation and modular training for impedance-based HAR.
