SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models

• URL: http://arxiv.org/abs/2507.06405v1
• Date: Tue, 08 Jul 2025 21:15:12 GMT
• Title: SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
• Authors: Lala Shakti Swarup Ray, Mengxi Liu, Deepika Gurung, Bo Zhou, Sungho Suh, Paul Lukowicz,
• Abstract summary: Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction.<n>We introduce SImpHAR, a novel framework addressing this limitation through two core contributions.<n>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.
• Score: 5.215675032361843
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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