Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease

TL;DR

This study develops a multimodal sensor protocol combining gait acceleration, EEG, EMG, and skin conductance to improve detection of freezing of gait in Parkinson's Disease, aiding fall prevention and understanding physiological transitions.

Contribution

It introduces a comprehensive multimodal data collection protocol for FOG detection and demonstrates the benefits of combining different sensor modalities for improved accuracy.

Findings

Multimodal data improves FOG detection accuracy.

EEG shows better discriminative ability than ACC and EMG.

Multimodal data helps study physiological transitions during FOG.

Abstract

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.