Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy.
Data-Driven Computer Engineering (D2iCE) Group, Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland.
Sensors (Basel). 2023 Apr 30;23(9):4426. doi: 10.3390/s23094426.
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
冻结步态（Freezing of gait，FoG）是帕金森病（Parkinson's disease，PD）的一种致残临床现象，其特征是尽管有行走的意图，但无法向前移动脚部。它是 PD 最麻烦的症状之一，导致跌倒风险增加和生活质量降低。可穿戴惯性传感器和机器学习（Machine learning，ML）算法的结合代表了在现实场景中监测 FoG 的可行解决方案。然而，传统的 FoG 检测算法不加区分地处理所有数据，而不考虑 FoG 发生时活动的上下文。本研究旨在开发一种轻量级、上下文感知的算法，该算法仅在某些情况下才能激活 FoG 检测系统，从而降低计算负担。实施了几种方法，包括 ML 和深度学习（Deep learning，DL）步态识别方法，以及基于加速度大小的单一阈值方法。为了训练和评估上下文算法，使用三个包含总共 81 名 PD 患者的不同数据集从单个惯性传感器中提取数据。步态识别的灵敏度和特异性分别为 0.95 至 0.96 和 0.80 至 0.93，一维卷积神经网络提供了最佳结果。在评估上下文意识对 FoG 检测性能的影响时，阈值方法的性能优于基于 ML 和 DL 的方法。总体而言，上下文算法允许丢弃超过 55%的非 FoG 数据和少于 4%的 FoG 发作。结果表明，上下文分类器可以降低 FoG 检测算法的计算负担，而不会显著影响 FoG 检测率。因此，实现上下文意识可以为在非卧床和自由生活环境中进行长期 FoG 监测提供一种节能解决方案。
