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

arXiv: https://arxiv.org/abs/2507.06405 · HTML: https://mlsanity.com/paper/2507.06405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text](https://mlsanity.com/paper/2210.14395/) (2022)
- [IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition](https://mlsanity.com/paper/2006.05675/) (2020)
- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments](https://mlsanity.com/paper/2212.11152/) (2022)
- [PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation](https://mlsanity.com/paper/2302.00391/) (2023)
- [iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition](https://mlsanity.com/paper/2402.09445/) (2024)
- [CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator](https://mlsanity.com/paper/2202.10562/) (2022)
- [Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUs](https://mlsanity.com/paper/2406.01316/) (2024)
- [Origami Single-end Capacitive Sensing for Continuous Shape Estimation of Morphing Structures](https://mlsanity.com/paper/2307.05370/) (2023)
- [Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR](https://mlsanity.com/paper/2402.14427/) (2024)
- [iFace: Hand-Over-Face Gesture Recognition Leveraging Impedance Sensing](https://mlsanity.com/paper/2403.18433/) (2024)

## Abstract (arXiv)

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.