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Session: Poster session I
Session starts: Thursday 24 January, 15:00

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Floris Beuving (Donders Centre for Neuroscience, Radboud University)
Ying Wang (Donders Centre for Neuroscience, Radboud University)
Rick Helmich (Donders Centre for Cognitive Neuroimaging, Radboud University)
Jorik Nonnekes (Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre (Radboudumc))
Mike X Cohen (Donders Institute for Brain, Cognition and Behaviour, Radboud University)
Richard van Wezel (Donders Centre for Neuroscience, Radboud University)

Abstract:
Freezing of gait (FOG) is a symptom of Parkinson’s disease patients. It can be described as a disruption of gait despite the intention to walk. Sensory stimulation with cues offers a remedy to tackle this symptom by helping patients to overcome FOG. However, it has been observed that on-demand cueing is more efficient to decrease the FOG duration compared to continuous cueing[1]. Therefore, this study aims to aid in the development of FOG detection and prediction algorithms. The experiment consisted of three movement in place tasks (stepping, normal half turning and rapid half turning) with a duration of two minutes for each session. All sessions were recorded on video and rated for the occurrence of FOG by two independent annotators. Fifteen idiopathic Parkinson’s disease patients were measured at off-medication (Hoehn and Yahr scale 2-4). Freezing was observed in all patients. In total 709 FOG episodes were detected with an average duration of 7 seconds. The equipment consisted of a TMSI Porti system (accelerometers, EMG, footswitches and ECG), and a 64 channel 10-10 EEG ActiCap system (Brainproducts). This study focuses on the correlation of body movements with FOG and non-FOG episodes. The uncovered correlations are used for multimodal data analysis to improve FOG detection. The multimodal data analysis allows modalities to complement each other[2], and is therefore interesting for investigating new perspectives for better FOG detection. The study is in progress and preliminary results will be presented.