A robot for verifying the precision of total reaction time measurement

Crocetta, Tânia Brusque; Kroich, Tiago; Thiesen, Argeu Carlos; Borges, Lucas R.; Júnior, Noé Gomes Borges; Abreu, Luíz Carlos de; Andrade, Alexandro

doi:10.1590/s1980-65742015000100004

Cited by 3 publications

(4 citation statements)

References 38 publications

(50 reference statements)

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“…An important finding of the current study relates to application development across mobile platforms, and that differences in hardware have the potential to significantly sway results dependent on temporal measures such as RT. Such alterations are expected to arise not only from differences in hardware and peripheral capabilities ( 35 ) but also background computing tasks ( 36 ) and even the method a user chooses to touch the screen ( 37 ). Due to such temporal differences observed across devices, feature selection for our mental acuity metric utilized features least likely to exhibit variability across devices.…”

Section: Discussionmentioning

confidence: 99%

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

2017

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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.

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Section: Discussionmentioning

confidence: 99%

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

2017

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“…Building a robot similar to that proposed by Neath et al 8 , Crocetta et al 9 validated the measurements obtained by the robot called Emboici DOI: dx.doi.org/10.7322/jhdg.88963 Robot: 46.95 ms (±6.04) at 1200 measures of TRT. The purpose of this robot was to react by pressing a button when a light stimulus was identified by a photodiode.…”

Section: Introductionmentioning

confidence: 80%

“…Constructed and validated by Crocetta et al 9 , this robot is composed of a photodiode that detects the change of brightness on the monitor (color stimulus generated by the software), triggering a digital servo motor that pushes a button (the space bar of the computer keyboard or the response panel button). This set is managed by an Arduino board (Figure 1-D Computer.…”

Section: Equipmentsmentioning

confidence: 99%

Validity of Software for Measurement of Total Reaction Time With Simple Stimulus -Trt_s 2012

Crocetta

Viana

Silva

et al. 2014

J. Hum. Growth Dev.

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Objective: Determine the validity of the TRT_S 2012 software in to assess the total reaction time (TRT) with a simple visual stimulus (TRTSimple) and mental fatigue from TRT (TRTFatigue). Methods: Three types of validation were applied: a) concurrent, for determining the correlation between the TRT_S 2012 Software and Vienna Test System (VTS), b) content of a sample of adults, and c)mechanical, using a robot that performs a mechanic motion to respond to a light stimulus.The study included 216 young adults, college students aged 17 -45 years (x = 24.0 ± 6.0) and a robot. Descriptive and inferential statistics were used for performance on TRT obtained by young adults and robot in two software. Results: The intra-class correlation in the adults TRT showed strong correlation between VTS and TRTSimple (R = .72). Identification of the proposed initial stimulus in TRTFatigue presented intermediate correlation with VTS (R = .56) and the final stimulus presented low correlation with VTS (R=.35).The robot TRT showed standard deviation ranging .5 ms (on average) between the highest and lowest.The standard error of the mean ranged from .23 to .28 and the distributions were homogeneous between 8.2 to 9.7%. Conclusion: The results confirmed the validity of the software TRT_S 2012 . It is a reliable cognitive test that can be applied to young adults for measuring the TRT with simple visual stimuli and for evaluating the influence of mental fatigue from the TRT. however, the delays caused by the computer resources used should be considered and measured with a resource like the robot. We conclude that the TRT_S 2012 software is valid for assessing the TRT and cognitive fatigue in healthy adults.Key words: reaction time, mental fatigue, software.

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“…The software records the time between the appearance of the stimulus and the press of the space bar as a simple TRT with visual stimulus. The software was used by Crocetta et al, 26 Crocetta et al, 27 Antunes et al, 28 Herrero et al, 29 and validated by Crocetta et al 30 in a sample of 216 young adults, together with a mechanical validation using a robotic arm Crocetta et al, 31 with satisfactory validity and reliability.…”

Section: Instrumentsmentioning

confidence: 99%

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

Antão

Abreu

Barbosa

et al. 2020

Cyberpsychology, Behavior, and Social Networking

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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.

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A robot for verifying the precision of total reaction time measurement

Cited by 3 publications

References 38 publications

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model

Validity of Software for Measurement of Total Reaction Time With Simple Stimulus -Trt_s 2012

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

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