通过打乱序列提升工业活动识别数据多样性，增强模型鲁棒性
SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition
- 用随机重排序列打乱时间依赖，提升数据分布一致性
- 准确率最高达70%±3%，宏F1达64%±1%
- 适合需要应对真实复杂场景的工业活动识别系统
在工业人体活动识别（HAR）领域，高质量且多样化的数据获取仍面临高昂成本与真实活动固有变异性两大挑战。本文通过注意力自编码器和条件生成对抗网络生成新数据集。针对数据异质性问题，提出一种打乱序列的增强策略，以统一数据分布。实验表明，随机序列重排显著提升分类性能，准确率达到0.70±0.03，宏F1为0.64±0.01。该方法通过破坏时间依赖，迫使模型聚焦于瞬时特征，从而增强对活动转换的鲁棒性。该策略不仅扩展了有效训练数据，也为复杂真实场景下的HAR系统提供了可行优化路径。
原文摘要 · Abstract (English)
In the realm of Human Activity Recognition (HAR), obtaining high quality and variance data is still a persistent challenge due to high costs and the inherent variability of real-world activities. This study introduces a generation dataset by deep learning approaches (Attention Autoencoder and conditional Generative Adversarial Networks). Another problem that data heterogeneity is a critical challenge, one of the solutions is to shuffle the data to homogenize the distribution. Experimental results demonstrate that the random sequence strategy significantly improves classification performance, achieving an accuracy of up to 0.70 $\pm$ 0.03 and a macro F1 score of 0.64 $\pm$ 0.01. For that, disrupting temporal dependencies through random sequence reordering compels the model to focus on instantaneous recognition, thereby improving robustness against activity transitions. This approach not only broadens the effective training dataset but also offers promising avenues for enhancing HAR systems in complex, real-world scenarios.
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