gait analysis

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait.


This is the raw data for the article "Algorithm for Gait Parameters Estimation Based on Heel-Mounted Inertial Sensors", which includes walking data recorded by IMUs and data recorded by an optical motion capture (OMC) system, where the experimental data consisted of three parts:

Validity Experiment Data includes gait data (both IMU and OMC) of 30 participants who were instructed to walk in a straight line within the motion capture area and return upon reaching the end to ensure a sufficient step count.


This dataset contains the output from 3D gait analysis. Over a period of 3 months, between January 1st and March 31st in 2019, 5 children were familiarized with the Hibbot by using the walking aid for 30 minutes, twice a week, under the supervision of a physiotherapist.


Gait analysis of people with transfemoral amputation is essential to support the rehabilitation process. In particular, the kinematics of the body center of mass (bCoM), derived from the motion of segments’ centers of mass (sCoM), provide crucial information about patients’ locomotion. Magneto-Inertial Measurement Units (MIMUs) may be adopted to obtain this information in-the-field. However, MIMUs provide the 3D acceleration of the origin of the sensor’s frame.