# A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

arXiv: https://arxiv.org/abs/2003.01874 · HTML: https://mlsanity.com/paper/2003.01874/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2402.01049/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition](https://mlsanity.com/paper/2406.16886/) (2024)
- [Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning](https://mlsanity.com/paper/2410.19769/) (2024)
- [CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator](https://mlsanity.com/paper/2202.10562/) (2022)
- [Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2306.05285/) (2023)
- [Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques](https://mlsanity.com/paper/2506.07612/) (2025)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices](https://mlsanity.com/paper/2402.03714/) (2024)
- [Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images](https://mlsanity.com/paper/2201.08958/) (2022)

## Abstract (arXiv)

Sensor-based human activity recognition (HAR) is now a research hotspot in multiple application areas. With the rise of smart wearable devices equipped with inertial measurement units (IMUs), researchers begin to utilize IMU data for HAR. By employing machine learning algorithms, early IMU-based research for HAR can achieve accurate classification results on traditional classical HAR datasets, containing only simple and repetitive daily activities. However, these datasets rarely display a rich diversity of information in real-scene. In this paper, we propose a novel method based on deep learning for complex HAR in the real-scene. Specially, in the off-line training stage, the AMASS dataset, containing abundant human poses and virtual IMU data, is innovatively adopted for enhancing the variety and diversity. Moreover, a deep convolutional neural network with an unsupervised penalty is proposed to automatically extract the features of AMASS and improve the robustness. In the on-line testing stage, by leveraging advantages of the transfer learning, we obtain the final result by fine-tuning the partial neural network (optimizing the parameters in the fully-connected layers) using the real IMU data. The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91.15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.