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Multimodal Data for the Detection of Freezing of Gait in Parkinson’s Disease

  • Wei Zhang
  • , Zhuokun Yang
  • , Hantao Li
  • , Debin Huang
  • , Lipeng Wang
  • , Yanzhao Wei
  • , Lei Zhang
  • , Lin Ma
  • , Huanhuan Feng
  • , Jing Pan
  • , Yuzhu Guo*
  • , Piu Chan*
  • *Corresponding author for this work
  • Capital Medical University
  • Xuzhou Medical University
  • Beihang University

Research output: Contribution to journalArticlepeer-review

Abstract

Freezing of gaits (FOG) is a very disabling symptom of Parkinson’s Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task.

Original languageEnglish
Article number606
JournalScientific Data
Volume9
Issue number1
DOIs
StatePublished - Dec 2022

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