Shi Bohan, Yen Shih Cheng, Tay Arthur, Tan Dawn M L, Chia Nicole S Y, Au W L
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5410-5415. doi: 10.1109/EMBC44109.2020.9175687.
Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.
冻结步态是帕金森病中最致残的步态障碍。在过去十年中，将机器学习和深度学习模型应用于可穿戴传感器数据以检测冻结步态发作的兴趣日益浓厚。在我们的研究中，我们招募了67名患有冻结步态的帕金森病患者，并在患者脚踝上佩戴两个无线惯性测量单元时进行了两项临床评估。我们将记录的时间序列传感器数据转换为连续小波变换尺度图，并训练了一个卷积神经网络来检测冻结发作。所提出的模型实现了89.2%的泛化准确率和88.8%的几何平均值。
