MovePort: Multimodal Dataset of EMG, IMU, MoCap and Insole Pressure for Analyzing Abnormal Movements and Postures in Rehabilitation Training

In most real world rehabilitation training, patients are trained to regain motion capabilities with the aid of functional/epidural electrical stimulation (FES/EES), under the support of gravity-assist systems to prevent falls. However, the lack of motion analysis dataset designed specifically for rehabilitation-related applications largely limits the conduct of pilot research. We provide an open access dataset, consisting of multimodal data collected via 16 electromyography (EMG) sensors, 6 inertial measurement unit (IMU) sensors, and 230 insole pressure sensors (IPS) per foot, together with a 26-sensor motion capture system, under different <i>MOVE</i>ments and <i>PO</i>stures for <i>R</i>ehabilitation <i>T</i>raining (<i>MovePort</i>). Data were collected under diverse experimental paradigms. Twenty four participants first imitated multiple normal and abnormal body postures including (1) normal standing still, (2) leaning forward, (3) leaning back, and (4) half-squat, which in practical applications, can be detected as feedback to tune the parameters of FES/EES and gravity-assist systems to keep patients in a target body posture. Data under imitated abnormal gaits, e.g., (1) with legs raised higher under excessive electrical stimulation, and (2) with dragging legs under insufficient stimulation, were also collected. Data under normal gaits with low, medium and high speeds are also included. Pathological gait data from a subject with spastic paraplegia further increases the clinical value of our dataset. We also provide source codes to perform both intra- and inter-participant motion analyses of our dataset. We expect our dataset can provide a unique platform to promote collaboration among neurorehabilitation engineers.

搜集汇总

背景与挑战

背景概述

MovePort是一个专门用于康复训练分析的多模态数据集，包含来自16个EMG传感器、6个IMU传感器、每脚230个鞋垫压力传感器和26个传感器的运动捕捉系统的数据，覆盖了正常和异常姿势、步态（如模仿过度电刺激或不足刺激下的步态）以及一个痉挛性截瘫患者的病理步态。该数据集由24名参与者提供，旨在支持神经康复工程研究，并提供源代码进行运动分析，以促进康复应用中的参数调整和协作。