Name
Abstract | ||
|---|---|---|
Virtual Reality (VR) sensorimotor rehabilitation is still in infancy but will soon require avatars, digital alter-egos of patients' physical selves. Such embodied interfaces could stimulate patients' perception in a rich and highly customized environment, where sensorimotor deficits, such as in Chemotherapy-Induced Peripheral Neuropathy, could be corrected. In such scenarios, motion prediction is a key ingredient for realistic immersion. Yet, such a task lives under hard processing latency constraints and the inherent variability of human motion. We propose a neural network meta-learning system exploiting the underlying correlations in body kinematics with potential to provide, within latency guarantees, personalized VR rehabilitation. The unsupervised meta-learner is able to extract underlying statistics of the motion data by exploiting data regularities in order to describe the underlying manifold, or structure, of motion under sensorimotor deficits. As avatars are patients' proxies in VR, and the direct extension of themselves into the virtual domain, their digital representations have to be naturally bound to their individual motion patterns and self-perception. Following this goal, we demonstrate, through preliminary experiments the potential of such a learning system for adaptive kinematics estimation in personalized rehabilitation VR avatars. Index TermsNeural Networks, Virtual Reality, Inverse Kinematics, MetaLearning, Rehabilitation |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBE.2019.00117 | 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) |
Field | DocType | ISSN |
Virtual reality,Kinematics,Inverse kinematics,Computer science,Embodied cognition,Artificial intelligence,Immersion (virtual reality),Artificial neural network,Perception,Avatar,Machine learning | Conference | 2471-7819 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristian Axenie | 1 | 0 | 0.34 |
| Armin Becher | 2 | 0 | 1.35 |
| Daria Kurz | 3 | 0 | 0.34 |
| Thomas Grauschopf | 4 | 0 | 1.35 |