Roessingh Research & Development-MyLeg database for activity prediction (MyPredict)
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Roessingh Research & Development-MyLeg database for activity prediction (MyPredict). The general aim of this database is to promote research in data-driven intent recognition strategies and activity prediction strategies in the lower-limb using electromyography and to promote research and development in the area of multi-array sEMG in the lower limb. The database contains three data sets, each containing kinematics and sEMG from able-bodied subjects. In total 55 subjects participated over 85 measurement sessions. Each data set contained a different sEMG measuring protocol containing either traditional bipolar sEMG or multi-array sEMG or a combination of both. In these data sets the subjects transitioned freely from one activity to the next, providing challenging data sets for activity recognition and providing the possibility to investigate human kinematics and sEMG during gait-related activities. This dataset is described in detail in Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects (Schulte et al.) MyPredict consists of three datasets, denoted by MP1XX, MP2XX and MP3XX.MyPredict 1: MP101-MP110, 10 able-bodied subjects (sex: 7m, 3f; age: 24±2 years; weight: 77±10 kg; height: 183±9cm), measured onceMyPredict 2: MP201-MP235, 35 able-bodied subjects (sex: 14m, 21f; age: 23±2 years; weight: 73±11 kg; height: 179±9 cm), measured onceMyPredict 3: MP301-MP310, 10 able-bodied subjects (sex: 4m, 6f; age: 24±2 years; weight: 71±9 kg; height: 174±6 cm), measured 4 times These files contain the measurement moment named `Day_X' with X the number of the measurement moment. Inside these measurement moments there are files called `Trial_YY', with YY the trial number, containing the different data types and `MVC' containing the EMG maximum voluntary contractions of each measurement moment. Note that only MyPredict 3 contains multiple measurement moments per subject. The different data types are acceleration (Acc), angular velocity (Gyr), joint angles (Ang), Orientation (Ori) and electromyography (EMG). Inside each file there are trials containing data arrays with the corresponding data. Data arrays are named as follows: Type_Side_Loc. Type is one of the six data types, Loc is the location of the sensor and Side is the side of the location, either Left, Right or empty. For example Ang_Right_Knee contains the 3D joint angles of the knee, Gyr_Pelvis contains the 3D angular velocity of the pelvis IMU and EMG_Left_VL contains the EMG data of the left vastus lateralis. Orientation is the orientation of the pelvis in space, expressed in Euler angles. Separate data types are 'Labels', which contains manual placed activity labels for each timestamp and 'Time' which indicates the timestamps per file. Marker data (Mrk) are stored in a separate group, `Markers' with their own `Time' array, as they have a different sample frequency (100Hz) compared to the other data types (1000Hz). Code supporting this dataset can be found in the github repository: github.com/Rvs94/MyPredict
Roessingh 研究与发展中心——用于活动预测的MyLeg数据库（MyPredict）。本数据库的核心宗旨为推动基于数据驱动的下肢意图识别策略与活动预测策略的研究，采集模态涵盖肌电信号，同时促进下肢多阵列表面肌电（surface electromyography, sEMG）领域的研发工作。 本数据库包含三个子数据集，均收录了健康受试者的运动学数据与表面肌电数据。总计有55名受试者参与了85次测量流程。每个子数据集采用了不同的表面肌电采集方案，涵盖传统双极表面肌电、多阵列表面肌电，或二者的组合形式。在数据采集过程中，受试者可自由在不同活动间切换，这为活动识别任务提供了具有挑战性的实测数据，同时也为研究步态相关活动中的人体运动学与表面肌电特征提供了可行条件。本数据集的详细信息可参见论文《Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects》（Schulte 等）。 MyPredict 包含三个子数据集，分别命名为 MP1XX、MP2XX 与 MP3XX： 1. MyPredict 1：对应编号范围 MP101-MP110，包含10名健康受试者（性别：男7名，女3名；年龄：24±2 岁；体重：77±10 kg；身高：183±9 cm），每位受试者仅接受1次测量。 2. MyPredict 2：对应编号范围 MP201-MP235，包含35名健康受试者（性别：男14名，女21名；年龄：23±2 岁；体重：73±11 kg；身高：179±9 cm），每位受试者仅接受1次测量。 3. MyPredict 3：对应编号范围 MP301-MP310，包含10名健康受试者（性别：男4名，女6名；年龄：24±2 岁；体重：71±9 kg；身高：174±6 cm），每位受试者接受4次测量。 各文件以`Day_X`命名，其中 X 代表测量场次的序号。在每个测量场次文件夹内，包含若干名为`Trial_YY`的文件（YY 为试次编号），存储各类实验数据；另有`MVC`文件，记录每次测量的肌电最大自主收缩量数据。请注意，仅 MyPredict 3 包含每位受试者的多次测量场次。 不同的数据类型包括加速度（Acc）、角速度（Gyr）、关节角度（Ang）、姿态（Ori）与肌电信号（EMG）。每个文件内的试次均包含对应的数据数组，命名规则为：`Type_Side_Loc`。其中 Type 为前述六种数据类型之一，Loc 为传感器的安装位置，Side 为肢体侧别，可选左（Left）、右（Right）或无特定侧别（empty）。例如，`Ang_Right_Knee` 代表右侧膝关节的三维关节角度数据，`Gyr_Pelvis` 代表骨盆惯性测量单元（Inertial Measurement Unit, IMU）的三维角速度数据，`EMG_Left_VL` 代表左侧股外侧肌的肌电数据。姿态（Ori）指骨盆在空间中的朝向，以欧拉角表示。 额外的数据类型包括`Labels`与`Time`：其中`Labels`包含每个时间戳对应的人工标注活动标签，`Time`代表每个文件的时间戳数组。标记数据（Mrk）存储在单独的`Markers`组中，并拥有独立的`Time`数组，原因是其采样频率为100Hz，与其他数据类型的1000Hz采样率不同。 本数据集的配套代码可在 GitHub 仓库获取：github.com/Rvs94/MyPredict
