Abstract：
Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson\'s disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. We attempted to establish an automatic FoG detection method from videos taken in uncontrolled environments such as in daily clinical practices. Motion features of 16 patients were extracted from timed-up-and-go test in 109 video data points, through object tracking and three-dimension pose estimation. These motion features were utilized to form the FoG detection model, which combined rule-based and machine learning-based models. The rule-based model distinguished the frames in which the patient was walking from those when the patient has stopped, using the pelvic position coordinates; the machine learning-based model distinguished between FoG and stop using a combined one-dimensional convolutional neural network and long short-term memory (1dCNN-LSTM). The model achieved a high intraclass correlation coefficient of 0.75-0.94 with a manually-annotated duration of FoG and %FoG. This method is novel as it combines object tracking, 3D pose estimation, and expert-guided feature selection in the preprocessing and modeling phases, enabling FoG detection even from videos captured in uncontrolled environments.
摘要：
步态冻结(FoG)是帕金森病及相关疾病患者的一种常见症状。最近开发了视频中的FoG检测；但是，该过程需要使用在受控环境中拍摄的视频。我们尝试从在日常临床实践等不受控制的环境中拍摄的视频中建立自动FoG检测方法。从109个视频数据点的定时上升和前进测试中提取了16例患者的运动特征，通过目标跟踪和三维姿态估计。这些运动特征被用来形成FoG检测模型，它结合了基于规则和基于机器学习的模型。基于规则的模型区分了患者行走的帧和患者停止时的帧，使用骨盆位置坐标;基于机器学习的模型使用组合的一维卷积神经网络和长短期记忆(1dCNN-LSTM)区分FoG和停止。该模型在FoG和%FoG的手动注释持续时间下实现了0.75-0.94的高组内相关系数。这种方法是新颖的，因为它结合了对象跟踪，3D姿态估计，以及预处理和建模阶段的专家指导特征选择，即使在不受控制的环境中捕获的视频也能实现FoG检测。
