M
Maureen Schmitter-Edgecombe
Researcher at Washington State University
Publications - 183
Citations - 8120
Maureen Schmitter-Edgecombe is an academic researcher from Washington State University. The author has contributed to research in topics: Cognition & Recall. The author has an hindex of 39, co-authored 172 publications receiving 6976 citations. Previous affiliations of Maureen Schmitter-Edgecombe include University of Memphis.
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The ecological validity of neuropsychological tests: a review of the literature on everyday cognitive skills.
TL;DR: Overall, the research suggests that many neuropsychological tests have a moderate level of ecological validity when predicting everyday cognitive functioning, and the strongest relationships were noted when the outcome measure corresponded to the cognitive domain assessed by the neuroPsychological tests.
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Costs of a predictable switch between simple cognitive tasks following severe closed-head injury
TL;DR: Findings indicate that participants with severe CHI were able to take advantage of time to prepare for the task switch, and the executive control processes involved in the switch costs were completed before the 1st trial of the run ended.
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Discovering Activities to Recognize and Track in a Smart Environment
TL;DR: This paper introduces an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine and can then track the occurrence of regular activities to monitor functional health and to detect changes in anindividual's patterns and lifestyle.
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Improving the ecological validity of executive functioning assessment
TL;DR: Results indicated that the group of executive functioning tests (i.e., Trail Making Test, Wisconsin Card Sorting Test, Stroop, and Controlled Oral Word Association Test) accounted for 18-20% of the variance in everyday executive ability as measured by the Dysexecutive Questionnaire and Brock Adaptive Functioning Questionnaire.
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Assessing the Quality of Activities in a Smart Environment
TL;DR: The results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology and will be useful for automating remote health monitoring and interventions.