AI 中文总结
针对帕金森冻结步态检测中的类别不平衡与多频挑战，提出WaveFoG小波门控Transformer，融合DWT多尺度特征与双分支编码器，在公开数据集上F1达0.875，优于单分支基线。
AI 中文摘要
冻结步态（FoG）是帕金森病（PD）最具致残性的发作性运动症状之一。基于腕戴式惯性信号的FoG检测面临诸多挑战，例如极端的数据类别不平衡（仅12-18%的信号窗口为FoG）、低频姿势不稳与具有诊断关键意义的3-8 Hz节律性震颤并存，以及需要受试者独立的泛化能力。为解决这些问题，本文提出WaveFoG，一种新颖的小波门控Transformer架构，它将离散小波变换（DWT）的多尺度表示与双分支编码器相结合。1D CNN捕获局部步态纹理，Transformer捕获全局时间上下文。两者通过sigmoid门控融合层进行融合，该层根据DWT子带描述符调制编码器输出。使用焦点损失进行训练，并在公开的Kaggle TLVMC FoG预测数据集上采用受试者独立的分组十折交叉验证（SI-CV）进行评估，WaveFoG实现了平均F1分数0.875±0.017和AUPRC 0.833±0.020，在F1上比单分支基线（1D CNN、Bi-LSTM和vanilla Transformer）高出最多5.4个百分点。时间显著性图突出了与FoG发作报告特征一致的时间模式。该项目的源代码可在以下网址获取：this https URL
英文摘要
Freezing of gait (FoG) is one of the most debilitating episodic motor symptoms of Parkinson's disease (PD). FoG detection from wrist worn inertial signals poses challenging problems such as extreme data class imbalance (only 12-18% of signal windows are FoG), low frequency postural instabilities alongside diagnostically crucial 3-8 Hz rhythmic tremors, and the need for subject independent generalisation. To address these, this paper presents WaveFoG, a novel wavelet gated Transformer architecture that combines multi scale representations from discrete wavelet transform (DWT) with a dual branch encoder. A 1D CNN captures local gait texture, and a Transformer captures global temporal context. They are fused via a sigmoid gating fusion layer that modulates the encoder output based on the DWT sub-band descriptors. Trained with focal loss and evaluated on the public Kaggle TLVMC FoG Prediction dataset under subject independent grouped ten fold cross validation (SI-CV), WaveFoG achieves a mean F1-score of 0.875 +/- 0.017 and AUPRC of 0.833 +/- 0.020, exceeding single branch baselines (1D CNN, Bi-LSTM, and vanilla Transformer) by up to 5.4 pp on F1. Temporal saliency maps highlight temporal patterns that are consistent with reported characteristics of FoG onset. The source code of this project is available at: https://github.com/Shihabul-Shuvo/WaveFoG
