Usability of a virtual reality environment simulating an automated teller machine for assessing and training persons with acquired brain injury

<p>Abstract</p> <p>Objective</p> <p>This study aimed to examine the usability of a newly designed virtual reality (VR) environment simulating the operation of an automated teller machine (ATM) for assessment and training.</p> <p>Design</p> <p>Part I involved evaluation of the sensitivity and specificity of a non-immersive VR program simulating an ATM (VR-ATM). Part II consisted of a clinical trial providing baseline and post-intervention outcome assessments.</p> <p>Setting</p> <p>A rehabilitation hospital and university-based teaching facilities were used as the setting.</p> <p>Participants</p> <p>A total of 24 persons in the community with acquired brain injury (ABI) - 14 in Part I and 10 in Part II - made up the participants in the study.</p> <p>Interventions</p> <p>In Part I, participants were randomized to receive instruction in either an "early" or a "late" VR-ATM program and were assessed using both the VR program and a real ATM. In Part II, participants were assigned in matched pairs to either VR training or computer-assisted instruction (CAI) teaching programs for six 1-hour sessions over a three-week period.</p> <p>Outcome Measures</p> <p>Two behavioral checklists based on activity analysis of cash withdrawals and money transfers using a real ATM were used to measure average reaction time, percentage of incorrect responses, level of cues required, and time spent as generated by the VR system; also used was the Neurobehavioral Cognitive Status Examination.</p> <p>Results</p> <p>The sensitivity of the VR-ATM was 100% for cash withdrawals and 83.3% for money transfers, and the specificity was 83% and 75%, respectively. For cash withdrawals, the average reaction time of the VR group was significantly shorter than that of the CAI group (p = 0.021). We found no significant differences in average reaction time or accuracy between groups for money transfers, although we did note positive improvement for the VR-ATM group.</p> <p>Conclusion</p> <p>We found the VR-ATM to be usable as a valid assessment and training tool for relearning the use of ATMs prior to real-life practice in persons with ABI.</p

Performance of the ATLAS Track Reconstruction Algorithms in Dense Environments in LHC Run 2

International audienceWith the increase in energy of the Large Hadron Collider to a centre-of-mass energy of 13  $\text {TeV}$ for Run 2, events with dense environments, such as in the cores of high-energy jets, became a focus for new physics searches as well as measurements of the Standard Model. These environments are characterized by charged-particle separations of the order of the tracking detectors sensor granularity. Basic track quantities are compared between 3.2 fb$^{-1}$ of data collected by the ATLAS experiment and simulation of proton–proton collisions producing high-transverse-momentum jets at a centre-of-mass energy of 13  $\text {TeV}$ . The impact of charged-particle separations and multiplicities on the track reconstruction performance is discussed. The track reconstruction efficiency in the cores of jets with transverse momenta between 200 and 1600 $\text {GeV}$ is quantified using a novel, data-driven, method. The method uses the energy loss, ${\text { d}}{} \textit{E}/d\textit{x}$ , to identify pixel clusters originating from two charged particles. Of the charged particles creating these clusters, the measured fraction that fail to be reconstructed is $0.061 \pm 0.006\ {\text {(stat.)}} \pm 0.014\ {\text {(syst.)}}$ and $0.093 \pm 0.017\ {\text {(stat.)}}\pm 0.021\ {\text {(syst.)}}$ for jet transverse momenta of 200–400  $\text {GeV}$ and 1400–1600  $\text {GeV}$ , respectively