A robot for verifying the precision of total reaction time measurement

Use of Augmented Reality with a Motion-Controlled Game Utilizing Alphabet Letters and Numbers to Improve Performance and Reaction Time Skills for People with Autism Spectrum Disorder

Augmented reality (AR) uses the real-world setting but enables a person to interact with virtual objects. In this study, we aimed to explore the use of alphabet letter and number in an AR task and its influence in reaction time in a population with autism spectrum disorders (ASD) compared with the performance of typical developing (TD) controls. We evaluated reaction time before and after AR tasks that consisted of identifying correct numbers and alphabet letters in 48 people with ASD and 48 with TD controls. Results indicate that total points for TD group were higher (M = 86.4 and M = 79.0) when compared with the ASD group (M = 54.5 and M = 51.5) for alphabet letters and numbers, respectively. Moreover, in analysis of reaction time results, only the ASD group showed an improvement in performance after the practice of an AR task. The control group was faster before (M = 553.7) and after (M = 560.5) when compared with the ASD group (M = 2616.0 and M = 2374.6, respectively). Despite the need for further studies, our results support that there is potential for clinical use of an AR task-based intervention for people with ASD.

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.