Dataset Open Access

Comprehensive Kinetic and EMG Dataset of Daily Locomotion with 6 types of Sensors

Huawei Wang; Akash Basu; Guillaume Durandau; Massimo Sartori

A human movement experiment with 12 young adults performing 13 daily movement trials (6 walking trials (speed: 0.9, 1.8, 2.7, 3.6, 4.5, 5.4 km/  h; 3 running trials (speed: 6.3, 8.8, 9.9 km/h); and  four non-locomotion trials (vertical jump, squat, lunge, and single leg landing) was conducted with the ethic approved by the University of Twente (ET/A.21.19298, reference number 2021.57). 

Six types of measurement devices were used to capture different information of participants’ movements. They can be divided into two systems: the wearable system and the conventional non-wearable system. In the wearable measurement system, eight IMUs (Xsens Link, Enschede, The Netherlands) were used to measure the kinematic movements of lower limbs and trunk. A pair of pressure insoles (Moticon, Munich, Germany) was used to measure the vertical GRF and CoPs. In the conventional system, an optical motion capture system (OMC) containing 8 infrared light cameras (6+ series, Qualisys, Gothenburg, Sweden) was used to measure body kinematics data using reflective markers. A split-belt instrumented treadmill (Motek-Forcelink B.V, Culemborg, The Netherlands) was used to measure the GRFs under each foot. Two video cameras were also included inside the conventional system to capture the RGB images of participants’ body postures at the sagittal and frontal planes ( In addition, nine electromyography sensors (EMGs) (Delsys Trigno, Delsys, USA) were included to record the activations of nine major muscles in the dominant leg ("soleus", "medial gastrocnemius", "lateral gastrocnemius", "tibialis anterior", " semimembranosus", " biceps femoris long head", "vastus lateral", "rectus femoris", "vastus medial").

In this shared data repository, both raw data (to be uploaded) and processed data ( are provided. The data processing pipeline can be found in this public GitHub repository: Guidelines for creating the same wearable system in the corresponding comparison study are shared in this GitHub repo:

Dataset structure descriptions:

Raw_data.rar: Raw dataset

  • Subjxx: subject folder
    • Qualisys: Qualisys project folder of the recordings
    • Xsens: Xsens project folder of the recordings
    • Insoles: Pressure insole project folder of the recordings

Processed_data.rar: Processed dataset.

  • allAverage.mat:  the overall summarization data of all subject at all movement trials. 
  • dataValidation.m: the Matlab code to plot the summarization data by loading the allAverage.mat.
  • subjs_info.txt: general information of all participants. 
  • ComparisonPlots: folder that contains the comparison plots between the laboratory-based system and the wearable measurement systems.
  • Subjxx: processed data for participants xx
    • dynMVCvalue.mat: the dynamic Maximal Voluntary Contraction of measured muscles (highest value among all recorded movements)
    • MVCvalue.mat: the Maximal Voluntary Contraction of measured muscles (highest value in MVC recording trial only)
    • Subjxx_xxxx_xx.mat: the processed data (generated by the above mentioned processing pipeline) of current subject at specific movement trial.
    • Qualisys: The exported mat files from the Qualisys recordings, including markers, EMGs, GRFs.
    • Xsens: The exported .mvnx files from the Xsens MVN software reprocessing. This file can be directly loaded by Matlab without requiring the Xsens license.
    • Insole: Insole recorded data, parsed from the Moticon endpoint SDK output.
    • OS: The folder that contains the scaled OpenSim model and corresponding .xml and data files for IK and ID processing. Majority content in this folder is automatically generated by the processing pipeline
    • Figures: plots of the processed data, including joint angles, GRFs, joint torques, and EMGs. They are all generated in the last module of the processing pipeline.

Structure of the Subjxx_xxxx_xx.mat:


  • Info: the general information of the participant and corresponding processing steps.
  • Marker: Marker data from Qualisys
  • Force: GRFs data from Qualisys
  • EMG: EMG data from Qualisys
  • IMU: Motion data from Xsens IMU system (from .mvnx)
  • Insole: The recorded pressure insole data
  • Resample: This data structure that contains the resampled data of the above mentioned sensor data
    • FrameRate: the sampling rate for all resampled sensor data
    • Marker: Resampled marker data
    • Force: resampled force data
    • EMG: resampled EMG data
    • IMU: resampled IMU data
    • Insole: resampled Insole data
    • CoM: resampled center of mass data from Xsens IMU system
    • Sych: this data structure contains the IK & ID data of two measurement systems. They are also synchronized by calculate the highest correlation coefficient. 
      • DeltaT: the time differences between the laboratory-based  system and the wearable measurement system.
      • IKAngData: the joint angle data from marker data inverse kinematics
      • ForcePlateGFRData: the ground reaction force data from instrumented treadmill
      • IDTrqData: the joint torque data from laboratory measurement system (optical + treadmill)
      • IMUAngData: the joint angle data from Xsens MVN software
      • InsoleGRFData: the ground reaction force data from pressure insoles
      • IDTrqData_portable: the joint torque data from the wearable system inverse dynamics
      • EMG: synchronized EMG data
      • CoM: synchronized CoM data
      • xxxxxLabel: the labels of each data column of corresponding data matrix
      • Average: this data structure contains the averaged gait/moment cycles
        • hsMatrix_right: The heel strike data points of the right leg
        • hsMatrix_left: the heel strike data points of the left leg
        • EMGAvedynNorFlag: whether dynamic MVC normalization is applied on EMG.
        • EMGAveNorFlag: whether MVC normalization is applied on EMG.
        • xxxx: The averaged data of corresponding variables
        • ForcePlateGRFDataInCalCn: transferred treadmill GRF data from the treadmill global coordinate to the local Calcaneus coordinate of the scaled OpenSim model. 
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