Subject

Gait analysis assesses the gait capabilities of a subject by quantifying kinematics (mostly joint angles) and kinetics (mostly joint moments) parameters. Spatiotemporal parameters (e.g. step length, step time and walking speed) are also popular in the rehabilitation field to assess for example the gait of elderly people and neurological patients (e.g. Stroke and Parkinson Disease).

Gait analysis based on marker based mocap - the state of the art - is performed using a biomechanical model, representing the human body as segments linked by balls joint in a chain. The orientation of the segments is determined by multiple cameras tracking the positions of infrared markers placed on the subject body. Marker based systems (e.g. Vicon) are therefore not portable and are difficult to use. Instead, the Kinect RGBD sensor offers a marker less, cheap, portable solution to track the subject body. Nevertheless, Kinect is limited by its inherent inaccuracy and the measured joint angles are generally not enough accurate for clinical assessment.

Kinect performs markerless motion capture by applying to single depth images a Machine Learning (ML) human pose estimation algorithm. The output can be inconsistent between frames (e.g. the lengths of model’s segments could change over time), and be affected by noise. Different approaches have been explored to refine the Kinect estimated pose, correct unnatural poses and integrate physical constraints in the kinematic model. Promising approaches are based on Recurrent Neural Network (RNN) such as Long short-term memory?(LSTM), which are able to make predictions in real time and do not require the time normalization of data.

Kind of work

The objective of this master thesis is to refine the joint angles measured by Kinect using a deep learning approach. Such deep learning model is trained using Kinect joint angles as the input and Vicon joint angles as targets. Different DL architectures will be explored, and their performances will be systematically evaluated. Motion data from healthy volunteers will be collected by the student in the Brubotics Rehabilitation Research Center, equipped with Vicon and Kinect systems.

The project will consist of:
- Literature study.
- Implementation of a DL model to refine the kinematic model estimated by Kinect.
- Development of a workflow to compare the angular waveforms obtained using different refinement DL models.
- Development and implementation of a protocol to collect Vicon and Kinect motion data from healthy volunteers.

The methods will be implemented in Python using open-source libraries and common deep learning frameworks. Data acquisition will require to operate the Vicon system (calibration, markers placement onto the subject body, use of the Vicon SDK software).

Framework of the Thesis

The work will be done at VUB-ETRO, Pleinlaan 9 and VUB-BRRC (Laarbeeklaan 121, 1090 Brussels)

Number of Students

1