用3D模拟生成生物阻抗数据，提升可穿戴设备动作识别精度
SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
- 通过3D人体模型与文本转动作生成仿真阻抗信号
- 在真实数据集上准确率提升22.3%，宏平均F1提升21.8%
- 适合做阻抗感知可穿戴系统研发的团队参考
基于可穿戴传感器的人体动作识别（HAR）在医疗、健身和人机交互中至关重要。生物阻抗传感具备精细动作捕捉的优势，但因标注数据稀缺而未被充分使用。本文提出SImpHAR框架，通过两项核心贡献解决此问题：其一，设计了一条仿真流程，利用最短路径估计、软体物理模拟及文本到动作生成技术，从3D人体网格生成逼真的生物阻抗信号，作为数据增强的数字孪生；其二，提出两阶段解耦训练策略，实现更广的动作覆盖，且无需合成数据与标签对齐。我们在自建的ImpAct数据集及两个公开基准上评估，结果表明该方法在准确率和宏平均F1上分别达到22.3%和21.8%的提升，显著优于现有方法。结果验证了仿真驱动增强与模块化训练在阻抗基HAR中的潜力。
原文摘要 · Abstract (English)
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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