用扩散模型生成惯性传感器动作数据，提升小样本下的识别效果
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems
- 基于扩散模型生成多变量时间序列动作数据
- 合成数据使分类器宏F1分数提升近30%
- 适合数据稀缺场景的动作识别研究者使用
惯性运动捕捉系统因便携性广受青睐，但特定动作的数据生成与标注成本高，且模型在数据有限时表现受限。为此，本文提出IMUDiffusion，一种专为多变量时间序列设计的概率扩散模型，可生成高质量、真实感强的人体动作时间序列。将真实数据与合成数据结合后，基准人体活动分类器性能显著提升，在某些情况下宏F1分数提高近30%。该方法为数据稀缺场景下的动作识别提供了有效解决方案。
原文摘要 · Abstract (English)
Kinematic sensors are often used to analyze movement behaviors in sports and daily activities due to their ease of use and lack of spatial restrictions, unlike video-based motion capturing systems. Still, the generation, and especially the labeling of motion data for specific activities can be time-consuming and costly. Additionally, many models struggle with limited data, which limits their performance in recognizing complex movement patterns. To address those issues, generating synthetic data can help expand the diversity and variability. In this work, we propose IMUDiffusion, a probabilistic diffusion model specifically designed for multivariate time series generation. Our approach enables the generation of high-quality time series sequences which accurately capture the dynamics of human activities. Moreover, by joining our dataset with synthetic data, we achieve a significant improvement in the performance of our baseline human activity classifier. In some cases, we are able to improve the macro F1-score by almost 30%. IMUDiffusion provides a valuable tool for generating realistic human activity movements and enhance the robustness of models in scenarios with limited training data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。