Tsinghua University, Beijing, China.
Hefei University of Technology, Hefei, China.
Artif Intell Med. 2024 Aug;154:102932. doi: 10.1016/j.artmed.2024.102932. Epub 2024 Jul 6.
Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors. Hence, we propose a novel multi-channel time-series neural network (MCT-Net) approach to merge multi-channel gait features into a comprehensive prediction framework, alerting patients to FOG symptoms in advance. Owing to the causal distributed convolution, MCT-Net is a real-time method available to give optimal prediction earlier and implemented in remote devices. Moreover, intra-channel and inter-channel transformers of MCT-Net extract and integrate different sensor position features into a unified deep learning model. Compared with four other state-of-the-art FOG prediction baselines, the proposed MCT-Net obtains 96.21% in accuracy and 80.46% in F1-score on average 2 s before FOG occurrence, demonstrating the superiority of MCT-Net.
冻结步态（FOG）是帕金森病的一个明显症状，表现为行动突然停顿，增加跌倒的风险。可穿戴多通道传感器系统是一种预测和监测 FOG 的有效方法，从而提醒使用者避免跌倒，提高生活质量。然而，现有的 FOG 预测方法主要集中在单一传感器系统上，无法处理多通道可穿戴传感器之间的干扰。因此，我们提出了一种新的多通道时间序列神经网络（MCT-Net）方法，将多通道步态特征合并到一个综合预测框架中，提前提醒患者出现 FOG 症状。由于因果分布式卷积，MCT-Net 是一种实时方法，可以更早地提供最佳预测，并在远程设备中实现。此外，MCT-Net 的通道内和通道间的转换器将不同传感器位置的特征提取并整合到一个统一的深度学习模型中。与其他四个最先进的 FOG 预测基线相比，所提出的 MCT-Net 在 FOG 发生前平均 2 秒内的准确率达到 96.21%，F1 得分为 80.46%，表现出了优越性。
