# MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model

arXiv: https://arxiv.org/abs/2009.09404 · HTML: https://mlsanity.com/paper/2009.09404/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport](https://mlsanity.com/paper/2310.09114/) (2023)
- [MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Prediction](https://mlsanity.com/paper/2312.10346/) (2023)
- [SMART: Scene-motion-aware human action recognition framework for mental disorder group](https://mlsanity.com/paper/2406.04649/) (2024)
- [Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2411.07828/) (2024)
- [Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition](https://mlsanity.com/paper/2208.07547/) (2022)
- [Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2504.00438/) (2025)
- [Refining Diffusion Models for Motion Synthesis with an Acceleration Loss to Generate Realistic IMU Data](https://mlsanity.com/paper/2512.08859/) (2025)

## Abstract (arXiv)

Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas. Recently, deep learning-based methods pave a new way of understanding and performing analysis of the complex data in the HAR system. However, the performance of these methods is mostly based on the quality and quantity of the collected data. In this paper, we innovatively propose to build a large database based on virtual IMUs and then address technical issues by introducing a multiple-domain deep learning framework consisting of three technical parts. In the first part, we propose to learn the single-frame human activity from the noisy IMU data with hybrid convolutional neural networks (CNNs) in the semi-supervised form. For the second part, the extracted data features are fused according to the principle of uncertainty-aware consistency, which reduces the uncertainty by weighting the importance of the features. The transfer learning is performed in the last part based on the newly released Archive of Motion Capture as Surface Shapes (AMASS) dataset, containing abundant synthetic human poses, which enhances the variety and diversity of the training dataset and is beneficial for the process of training and feature transfer in the proposed method. The efficiency and effectiveness of the proposed method have been demonstrated in the real deep inertial poser (DIP) dataset. The experimental results show that the proposed methods can surprisingly converge within a few iterations and outperform all competing methods.