Multimodal Dataset of Freezing of Gait in Parkinson's Disease

Freezing of gaits (FOG), a debilitating transitory inability to pursue moving, is one of the severest symptoms of Parkinson's disease (PD). An accurate and reliable detection or prediction of FOG is of great significance for PD patients' assessment and rehabilitation. It is difficult to detect FOG with sufficient low-latency and high precision based on single sensor information. In order to improve the detection accuracy and facilitate further research, we gathered and presented a new multimodal dataset by combining rich physical and physiological sensor information. The multimodal data, including electroencephalogram (EEG), electromyogram (EMG), electrocardiogram (ECG), skin conductance (SC), and acceleration (ACC) in walking tasks, were collected using a high-quality hardware system integrated commercial and self-designed sensors. A standard experimental procedure was carefully designed to induce FOG in hospital surroundings. A total number of 12 PD patients completed the experiments and produced a total length of 3 hours and 42 minutes valid data. The FOG episodes in the multimodal data were labeled by two qualified physicians. The multimodal data can be used to efficiently discriminant FOG from normal locomotion, and indicated that changes in the multimodal motional and electrophysiological signals during FOG episodes could be used to guide PD patients' treatment and recovery.

步态冻结（Freezing of Gait, FOG）是一种使人衰弱的暂时性运动障碍，也是帕金森病（Parkinson's Disease, PD）最严重的症状之一。对步态冻结进行准确可靠的检测与预测，对于帕金森病患者的病情评估与康复干预具有重要意义。仅依靠单一传感器信息，难以实现低延迟、高精度的步态冻结检测。为提升检测精度并方便后续研究，我们收集并发布了一款新型多模态数据集，整合了丰富的物理与生理传感器信息。该多模态数据包含行走任务中的脑电图（Electroencephalogram, EEG）、肌电图（Electromyogram, EMG）、心电图（Electrocardiogram, ECG）、皮肤电（Skin Conductance, SC）与加速度（Acceleration, ACC）数据，采集所用的高质量硬件系统集成了商用传感器与自研传感器。研究人员精心设计了标准实验流程，在医院环境中诱导受试者出现步态冻结现象。共有12名帕金森病患者完成了实验，最终获得总时长为3小时42分钟的有效数据。该多模态数据中的步态冻结发作片段，均由两名资质合格的医师进行标注。该数据集可有效区分步态冻结与正常行走行为，同时表明步态冻结发作期间多模态运动与电生理信号的变化，能够为帕金森病患者的治疗与康复提供指导。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态的帕金森病步态冻结（FOG）数据集，包含EEG、EMG、ECG等多种传感器数据，旨在提高FOG检测的准确性。数据来自12名患者，总时长3小时42分钟，适用于帕金森病患者的评估和康复研究。