1. Convolutional Neural Network for Freezing of Gait Detection Leveraging the Continuous Wavelet Transform on Lower Extremities Wearable Sensors Data
- Author
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Shih-Cheng Yen, Wing-Lok Au, Arthur Tay, Dawn Tan, Nicole Shuang-Yu Chia, and Bohan Shi
- Subjects
Computer science ,Wavelet Analysis ,Wearable computer ,02 engineering and technology ,Convolutional neural network ,03 medical and health sciences ,Wearable Electronic Devices ,0302 clinical medicine ,Gait (human) ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Gait disorders ,Computer vision ,Gait ,Continuous wavelet transform ,Gait Disorders, Neurologic ,Artificial neural network ,Gait Disturbance ,business.industry ,Deep learning ,Parkinson Disease ,Lower Extremity ,020201 artificial intelligence & image processing ,Artificial intelligence ,Neural Networks, Computer ,business ,030217 neurology & neurosurgery - Abstract
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%.
- Published
- 2020