The use of the commercial video games as rehabilitation tools, such as the Nintendo WiiFit, has recently gained much interest in the physical therapy arena. Motion tracking controllers such as the Nintendo Wiimote are not sensitive enough to accurately measure performance in all components of balance. Additionally, users can figure out how to "cheat" inaccurate trackers by performing minimal movement (e.g. wrist twisting a Wiimote instead of a full arm swing). Physical rehabilitation requires accurate and appropriate tracking and feedback of performance. To this end, we are developing applications that leverage recent advances in commercial video game technology to provide full-body control of animated virtual characters. A key component of our approach is the use of newly available low cost depth sensing camera technology that provides markerless full-body tracking on a conventional PC. The aim of this research was to develop and assess an interactive game-based rehabilitation tool for balance training of adults with neurological injury.

The field of rehabilitation generally stands to benefit from the continual advances in VR technology, concomitant system cost reductions and an expanding clinical research literature and knowledge base. Home-based activity within VR systems that are low-cost, easy to deploy and maintain, and meet the requirements for "good" interactive rehabilitation tasks could radically improve users' access to care, adherence to prescribed training and subsequently enhance functional activity in everyday life in clinical populations.

Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique which extracts accounts that show similar transaction patterns. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors.