提升卒中后活动识别能力：用于运动学数据增强的生成对抗网络
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation
AI总结：
针对卒中康复可穿戴监测的机器学习模型泛化性受数据限制的问题，本研究用cGAN生成合成运动学数据扩充训练集，使任务分类准确率从66.1%提升至80.0%，助力更精准的康复监测与干预。
摘要：
用于卒中康复可穿戴监测的机器学习（ML）模型的泛化性通常受限于可用数据规模有限且异质性强的问题。数据增强通过向真实数据中添加计算生成的数据来丰富训练集的变异性，从而应对这一挑战。传统增强方法（如旋转、置换和时间扭曲）在提升分类器性能方面已显现出一定效果，但往往无法生成符合真实情况的训练样本。本研究采用条件生成对抗网络（cGANs），基于一个公开数据集生成合成运动学数据，高度模拟实验测量的卒中幸存者伸手动作。该方法不仅捕捉了卒中后复杂的时间动态特征和常见运动模式，还显著扩充了训练数据集。通过在合成数据与实验数据的混合集上训练深度学习模型，我们提升了任务分类准确率：融合合成数据的模型整体准确率达80.0%，显著高于仅用真实数据训练的模型的66.1%。这些改进实现了更精准的任务分类，为临床医生更准确地监测患者进展、更有效地定制康复干预方案提供了可能。
英文摘要：
The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples. This study employs Conditional Generative Adversarial Networks (cGANs) to create synthetic kinematic data from a publicly available dataset, closely mimicking the experimentally measured reaching movements of stroke survivors. This approach not only captures the complex temporal dynamics and common movement patterns after stroke, but also significantly enhances the training dataset. By training deep learning models on both synthetic and experimental data, we enhanced task classification accuracy: models incorporating synthetic data attained an overall accuracy of 80.0%, significantly higher than the 66.1% seen in models trained solely with real data. These improvements allow for more precise task classification, offering clinicians the potential to monitor patient progress more accurately and tailor rehabilitation interventions more effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
