Early detection methods for cognitive impairment are crucial for its effective treatment. Dual-task-based pipelines that rely on skeleton sequences can detect cognitive impairment reliably. Although such pipelines achieve state-of-the-art results by analyzing skeleton sequences of periodic stepping motion, we propose that their performance can be improved by decomposing the skeleton sequence into representative phase-aligned periods and focusing on them instead of the entire sequence. We present the phase-aligned periodic graph convolutional network, which is capable of processing phase-aligned periodic skeleton sequences. We trained it with a cross-modality feature fusion loss using a representative dataset of 392 samples annotated by medical professionals. As part of a dual-task cognitive impairment detection pipeline that relies on two-dimensional skeleton sequences extracted from RGB images to improve its general usability, our proposed method outperformed existing approaches and achieved a mean sensitivity of 0.9231 and specificity of 0.9398 in a four-fold cross-validation setup.

中文翻译：

---

PPGCN：用于基于双任务的认知障碍检测的相位对齐周期图卷积网络

认知障碍的早期检测方法对其有效治疗至关重要。依赖骨架序列的基于双任务的管道可以可靠地检测认知障碍。尽管此类管道通过分析周期性步进运动的骨架序列实现了最先进的结果，但我们提出，可以通过将骨架序列分解为代表性的相位对齐周期并关注它们而不是整个序列来提高它们的性能。我们提出了相位对齐周期图卷积网络，它能够处理相位对齐周期骨架序列。我们使用由医疗专业人员注释的 392 个样本的代表性数据集，通过跨模态特征融合损失对其进行训练。作为双任务认知障碍检测管道的一部分，该管道依赖于从 RGB 图像中提取的二维骨架序列来提高其一般可用性，我们提出的方法优于现有方法，并在四项测试中实现了 0.9231 的平均灵敏度和 0.9398 的特异性。折叠交叉验证设置。