Virtual Falls

Abstract

In this paper, we present an innovative application of Virtual Reality in human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts into developing reliable and effective fall detection algorithms and devices by researchers and engineers, minimal success has been seen. The lack of recorded fall data and the data quality have been identified as a major obstacle. To address this issue, we are proposing a framework for generating fall data in virtual environments. Our initial results have indicated that the virtual fall data generated using the proposed framework are of sufficient quality and could be used to improve fall detection algorithms. Although the approach proposed is to be used for fall detection, it is fully applicable to other domains that require training data.