Developing a Multidimensional Approach to Assess Impairment in Skilled Environments
Developing a Multidimensional Approach to Assess Impairment in Skilled Environments
批准号:
543474-2019
负责人:
Singhal, AnthonyAB
金额:
$4.33万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31
中文摘要
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英文摘要
The risks associated with cognitive impairment can be severe across the lifespan and are related to many factors such as illness, aging, injury, and substance use. Along with professional discretion and standardized practice, neuropsychological and psychometric testing have been typical methods for assessing risk of this nature. This is particularly the case in the workplace and in highly skilled environments. With recent and exciting advances in cognitive neuroscience, and big data analytics it is now possible to re-vision the practical approach to assessing risk. We now have the capability to develop computational algorithms to simulate large-scale networks underlying human behavior in ways not possible from traditional assessment techniques. Moreover, this machine learning approach can be a powerful tool for predicting specific real-world behavior based on a relative combination of human data from domains such as neuroscience, cognition, physiology, behavior, skilled performance, and medical history. DriveABLE is a Canadian company in the business of evaluating real-world impairment risk with cognitive science solutions for a range of industry partners. They are interested in developing a set of tools that cover a full range of human cognition and performance that are highly predictive of real-world behavior in many workplace scenarios. Their goals are to develop reliable solutions to assess impairment risk as it applies to transportation, law enforcement, cannabis use, and other safety-sensitive environments. The primary goal of this proposal is to continue to foster an established relationship between my research program at the University of Alberta, and DriveABLE. The long-term goal is to use cognitive neuroscience and psychophysiology research methods along with machine learning to understand the "blueprint" of human performance in skilled real-world environments such that a set of tools will be developed for DriveABLE to continue their growth as an industry leader in evaluating real-world impairment risk.
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