  The accurate and reliable detection or prediction of freezing of gaits (FOG)
is important for fall prevention in Parkinson's Disease (PD) and studying the
physiological transitions during the occurrence of FOG. Integrating both
commercial and self-designed sensors, a protocal 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 total length of 3 hours and 42 minutes
of valid data. The FOG episodes were labeled by two qualified physicians. Each
unimodal data and combinations have been used to detect FOG. Results showed
that multimodal data benefit the detection of FOG. Among unimodal data, EEG had
better discriminative ability than ACC and EMG. However, the acquisition of EEG
are more complicated. Multimodal motional and electrophysiological data can
also be used to study the physiological transition process during the
occurrence of FOG and provide personalised interventions.
