Alertness Level

2012 ◽  
Keyword(s):  
Alertness Level

2B-Alert Web 2.0: An Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine (Preprint)

2021 ◽  
Author(s):  
Jaques Reifman ◽  
Kamal Kumar ◽  
Luke Hartman ◽  
Andrew Frock ◽  
Tracy J. Doty ◽  
...  
Keyword(s):  
Night Shift ◽  
Sleep Loss ◽  
Time Of Day ◽  
Caffeine Consumption ◽  
Fatigue Management ◽  
Potential Benefits ◽  
Computer Based ◽  
Evening Shift ◽  
Alertness Level ◽  
The U.S

BACKGROUND One-third of the U.S. population experiences sleep loss, with the potential to impair physical and cognitive performance, and result in reduced productivity and imperil safety during work and daily activities. Computer-based fatigue-management systems, with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits, could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released the 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE Here, we aimed to enhance the capability of the 2B-Alert Web by providing the means for the tool to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times, under any sleep-loss condition. METHODS We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm’s recommendations with those based on the U.S. Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS For the four simulated conditions, the 2B-Alert Web-proposed interventions increased average alertness by 36 to 94% and decreased peak alertness impairment by 31 to 71%, while using equivalent or smaller doses of caffeine as the corresponding U.S. Army guidelines. CONCLUSIONS The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work/rest schedules. 2B-Alert Web is accessible at: <https://2b-alert-web.bhsai.org>.

Alertness Level

2008 ◽  
pp. 75-77
Author(s):  
Hans P. A. Van Dongen ◽  
Gregory Belenky
Keyword(s):  
Alertness Level

Detection of driver's alertness level based on the Viola and Jones method and logistic regression analysis

2019 ◽  
Vol 6 (2/3/4) ◽  
pp. 356 ◽  
Author(s):  
Samir Allach ◽  
Mohamed Ben Ahmed ◽  
Anouar Abdelhakim Boudhir
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Alertness Level

Effects of Color Temperature of Light on the Alertness Level

1998 ◽  
Vol 82 (Appendix) ◽  
pp. 220-220
Author(s):  
Akihiro Michimori ◽  
Kazunori Araki ◽  
Hiroyuki Inbe ◽  
Hiroshi Hagiwara ◽  
Toshihiko Sakaguchi
Keyword(s):  
Color Temperature ◽  
Alertness Level

Correlation between vehicle's meandering and driver's alertness level

JSAE Review ◽  
1995 ◽  
Vol 16 (1) ◽  
pp. 49-54 ◽  
Author(s):  
K Yamamoto
Keyword(s):  
Alertness Level

Real-time monitoring of the human alertness level

2003 ◽  
Author(s):  
Robin Alvarez ◽  
Francisco del Pozo ◽  
Elena Hernando ◽  
Eduardo Gomez ◽  
Antonio Jimenez
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
Alertness Level

scholarly journals Method of Acute Alertness Level Evaluation after Exposure to Blue and Red Light (based on EEG): Technical Aspects

2018 ◽  
Author(s):  
Agnieszka Wolska ◽  
Dariusz Sawicki ◽  
Kamila Nowak ◽  
Mariusz Wisełka ◽  
Marcin Kołodziej
Keyword(s):  
Red Light ◽  
Technical Aspects ◽  
Alertness Level

Fuzzy Analysis of Speech Metrics to Estimate Conductor Alertness

2009 ◽  
Author(s):  
Parham Shahidi ◽  
Steve C. Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Rule Base ◽  
Mathematical Framework ◽  
Human Knowledge ◽  
Knowledge Based ◽  
Single Output ◽  
Output System ◽  
Alertness Level ◽  
Different Levels

A Fuzzy Logic-based algorithm has been developed for processing a series of speech metrics with the ultimate goal of estimating train conductor alertness. The output is a single metric, which directly quantifies the alertness level of the conductor. The metrics were selected based on their correlation to alertness through processed speech, but without any interpretation of the spoken words or phrases. Metrics that are used include: speech duration, silence duration, word production rate and word intensity. The assessment of these metrics is an experience and human knowledge based task, which generates the need for a mathematical model to accommodate this special circumstance. The algorithm developed here uses Fuzzy Logic to cast the human knowledge base into a mathematical framework for the alertness estimation analysis. The core of this fuzzy system is a rule base consisting of fuzzy IF-THEN rules, which are derived from the existing knowledge about the effects of sleep deprivation on alertness such as Furthermore, the rules were inferred from actual voice recordings that were taken on board a train. This data was then used to create a classification scheme to determine which pattern in the speech indicates different levels of alertness from anxiety to fatigue. The simplicity of the underlying mathematical model in this approach enables this system to compute and output an alertness metric in real-time. The nature of this algorithm allows for the use of an arbitrary number of rules to classify the alertness level and therefore provides the ability to continuously develop and extend the rule base as new knowledge emerges. The resulting algorithm is a fast, multi-input, single-output system that is able to quantify the train conductor’s alertness level anytime speech is produced.

Objective Measurement of the Alertness Level in Dementia

2003 ◽  
Vol 15 (4) ◽  
pp. 212-217 ◽  
Author(s):  
Åke Edman ◽  
Martin Brunovsky ◽  
Magnus Sjögren ◽  
Anders Wallin ◽  
Milos Matousek
Keyword(s):  
Objective Measurement ◽  
Alertness Level
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