MovePort: Multimodal Dataset of EMG, IMU, MoCap and Insole Pressure for Analyzing Abnormal Movements and Postures in Rehabilitation Training
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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.
在绝大多数实际临床康复训练场景中，患者需借助功能性电刺激/硬膜外电刺激（FES/EES），并在重力辅助系统的支撑下预防跌倒，以此开展运动功能恢复训练。然而，目前缺乏专门面向康复相关应用的运动分析数据集，这极大制约了先导研究的推进。本研究公开了一款开放获取数据集，该数据集通过16个肌电（EMG）传感器、6个惯性测量单元（IMU）传感器、每只足部搭载的230个鞋垫压力传感器（IPS），以及一套配备26个传感器的运动捕捉系统采集得到，采集场景覆盖康复训练（MovePort）中的各类运动与姿态。数据采集采用了多样化的实验范式：24名受试者首先模仿了多种正常与异常身体姿态，包括（1）静止直立站立、（2）身体前倾、（3）身体后倾以及（4）半蹲姿态；在实际应用中，这些姿态可作为反馈信号，用于调节FES/EES与重力辅助系统的参数，使患者维持目标身体姿态。研究还采集了模仿的异常步态数据，例如（1）电刺激过量时的高抬腿步态，以及（2）刺激不足时的拖曳步态。数据集同时涵盖了低速、中速与高速的正常步态数据。一名痉挛性截瘫患者的病理步态数据进一步提升了本数据集的临床应用价值。本数据集还附带了用于开展受试者内与受试者间运动分析的源代码。本数据集有望为神经康复工程领域的研究者提供一个独特的协作平台，推动相关领域的学术合作与技术发展。
