Abbasi Sara, Rezaee Khosro
Department of Biomedical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran.
Department of Biomedical Engineering, Meybod University, Meybod, Iran.
Brain Behav. 2025 Jan;15(1):e70206. doi: 10.1002/brb3.70206.
A debilitating and poorly understood symptom of Parkinson's disease (PD) is freezing of gait (FoG), which increases the risk of falling. Clinical evaluations of FoG, relying on patients' subjective reports and manual examinations by specialists, are unreliable, and most detection methods are influenced by subject-specific factors.
To address this, we developed a novel algorithm for detecting FoG events based on movement signals. To enhance efficiency, we propose a novel architecture integrating a bottleneck attention module into a standard bidirectional long short-term memory network (BiLSTM). This architecture, adaptable to a convolution bottleneck attention-BiLSTM (CBA-BiLSTM), classifies signals using data from ankle, leg, and trunk sensors.
Given three movement directions from three locations, we reduce computational complexity in two phases: selecting optimal channels through ensemble learning followed by feature reduction using attention mapping. In FoG event detection tests, performance improved significantly compared to control groups and existing methods, achieving 99.88% accuracy with only two channels.
The reduced computational complexity enables real-time monitoring. Our approach demonstrates substantial improvements in classification results compared to traditional deep learning methods.
帕金森病（PD）中一种使人衰弱且了解不足的症状是步态冻结（FoG），它会增加跌倒风险。基于患者主观报告和专家手动检查的FoG临床评估不可靠，并且大多数检测方法受个体因素影响。
为解决此问题，我们开发了一种基于运动信号检测FoG事件的新算法。为提高效率，我们提出一种将瓶颈注意力模块集成到标准双向长短期记忆网络（BiLSTM）中的新架构。这种架构适用于卷积瓶颈注意力 - BiLSTM（CBA - BiLSTM），使用来自脚踝、腿部和躯干传感器的数据对信号进行分类。
对于来自三个位置的三个运动方向，我们分两个阶段降低计算复杂度：通过集成学习选择最优通道，然后使用注意力映射进行特征约简。在FoG事件检测测试中，与对照组和现有方法相比性能显著提高，仅使用两个通道时准确率达到99.88%。
降低的计算复杂度实现了实时监测。与传统深度学习方法相比，我们的方法在分类结果上有显著改进。
