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.

在绝大多数现实场景的康复训练中，患者借助功能性电刺激/硬膜外电刺激（functional/epidural electrical stimulation, FES/EES）开展运动功能恢复训练，并依托重力辅助系统防止跌倒。然而，目前缺乏专门面向康复相关应用的运动分析数据集，这极大限制了相关先导研究的开展。本研究公开了一款开放获取数据集，其包含多模态数据：采集设备包括16通道肌电（electromyography, EMG）传感器、6个惯性测量单元（inertial measurement unit, IMU）、每只足部搭载230个鞋垫压力传感器（insole pressure sensor, IPS），以及一套含26个捕捉节点的运动捕捉系统；数据采集覆盖康复训练（Rehabilitation Training, MovePort）场景下的各类运动（MOVE）与姿态（PO）。数据采集采用多种实验范式：24名受试者首先完成多种正常与异常身体姿态的模仿动作，具体包括(1) 静止站立、(2) 前倾、(3) 后倾、(4) 半蹲；在实际应用中，这类姿态数据可作为反馈信号，用于调节FES/EES与重力辅助系统的参数，从而将患者维持在目标身体姿态。研究同时采集了模仿异常步态的数据，例如(1) 电刺激过强导致的抬腿过高步态、(2) 刺激不足导致的拖曳步态。此外，数据集还涵盖低、中、高三种速度下的正常步态数据。1名痉挛性截瘫患者的病理性步态数据进一步提升了本数据集的临床应用价值。本研究还提供了用于开展受试者内与受试者间运动分析的源代码。我们期望本数据集能够为神经康复工程领域的研究者搭建专属协作平台，推动相关领域的合作与发展。