Inverse kinematics for full-body self representation in VR-based cognitive rehabilitation

Being self-represented through an avatar increases embodiment and the feeling of presence in virtual reality. Nevertheless, currently users in VR are typically represented only by their hands, as not enough tracking data is available for full body self-representation. In our use case of VR-based diagnostics and cognitive rehabilitation of stroke patients, we aim for full body self-representation in order to increase therapeutic effectivity of the treatment. To solve this problem, Inverse Kinematics (IK) can be used for pose estimation, where no tracking data is available. IK allows to minimize the use of hardware and efforts of patients and clinical staff and, at the same time, provides a full-body representation based only on positions of the VR-HMD and users hands as input. In some use cases tracking data from additional, visual full body tracking sensors can be used to estimate the position of the lower body joints. In this study, we evaluate existing IK-based pose estimators; find that VRIK from Final IK is the most suitable approach for the given use case; integrate VRIK in our VR-rehabilitation system; adapt VRIK to meet the use case requirements and conduct subjective tests to validate a significantly increased notion of embodiment and presence through full-body over hands-only self-representation.