Gait data from 51 healthy participants with motion capture, inertial measurement units, and computer vision

Abstract

We present a dataset comprising motion capture, inertial measurement unit data, and sagittal-plane video data from walking at three different instructed speeds (slow, comfortable, fast). The dataset contains 51 healthy participants with approximately 60 walking trials from each participant.Each walking trial contains data from motion capture, inertial measurement units, and computer vision. Motion capture data comprises ground reaction forces and moments from floor-embedded force plates and the 3D trajectories of subject-worn motion capture markers. Inertial measurement unit data comprises 3D accelerometer readings and 3D orientations from the lower limbs and pelvis. Computer vision data comprises 2D keypoint trajectories detected using the OpenPose human pose estimation algorithm from sagittal-plane video of the walking trial. Additionally, the dataset contains participant demographic and anthropometric information such as mass, height, sex, age, lower limb dimensions, and knee intercondylar distance measured from magnetic resonance images.The dataset can be used in musculoskeletal modelling and simulation to calculate kinematics and kinetics of motion and to compare data between motion capture, inertial measurement, and video capture.

Keywords

• Gait data
• Inertial measurement
• Video capture
• Gait analysis
• Openpose