Delgado-Terán Juan Daniel, Hilbrants Kjell, Mahmutović Dzeneta, Silva de Lima Ana Lígia, Wezel Richard J A van, Heida Tjitske
TechMed Centre, Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands.
Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands.
Sensors (Basel). 2025 Mar 18;25(6):1895. doi: 10.3390/s25061895.
Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson's Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient's ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments.
冻结步态（FOG）是帕金森病（PD）患者经历的一种运动症状，患者感觉自己像被粘在地板上一样。为了进行有效的提示以预防或缩短冻结步态发作，需要准确且持续的检测。开发了一种卷积神经网络（CNN），用于在半自由生活条件下检测PD患者脚踝上的惯性测量单元（IMU）记录的数据中的冻结步态发作。数据被分为两组：一组包含所有运动，另一组包含与冻结步态检测相关的行走和转弯活动。使用五折交叉验证（5Fold-CV）、留一受试者交叉验证（LOSO-CV）以及准确性、敏感性、精确性、F1分数和曲线下面积（AUROC）等性能指标对CNN模型进行评估；收集了24名PD参与者的数据，排除了三名没有冻结步态发作的参与者。对于行走和转弯活动，CNN模型在5Fold-CV中的AUROC = 0.9596，在LOSO-CV中的AUROC = 0.9275。当纳入所有活动时，5Fold-CV中的AUROC降至0.8888，LOSO-CV中的AUROC降至0.9017；该模型在相关运动场景中能有效检测冻结步态，但在半自由生活环境中难以将冻结步态与其他非活动状态（如坐着和站立）区分开来。
