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Detection of Freezing of Gait using Convolutional Neural Networks and Data from Lower Limb Motion Sensors
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  • Bohan Shi ,
  • Arthur Tay ,
  • Dawn M.L. Tan ,
  • Nicole S.Y. Chia ,
  • W.L. Au ,
  • Shih-Cheng Yen
Bohan Shi
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Arthur Tay
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Dawn M.L. Tan
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Nicole S.Y. Chia
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Shih-Cheng Yen
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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%.
Jul 2022Published in IEEE Transactions on Biomedical Engineering volume 69 issue 7 on pages 2256-2267. 10.1109/TBME.2022.3140258