Autonomous unobtrusive detection of mild cognitive impairment in older adults

IEEE Transactions on Biomedical Engineering

Volumn 62, Issue 5, 2015, Pages 1383-1394

  Akl, Ahmad ^a   Taati, Babak ^b   Mihailidis, Alex ^a,c  

^a UNIVERSITY OF TORONTO   (Canada)

^b TORONTO REHABILITATION INSTITUTE   (Canada)

^c UNIVERSITY OF TORONTO   (Canada)

Author keywords

Home Activity; Machine Learning; Mild Cognitive Impairment; Older Population; Signal Processing; Smart Systems; Unobtrusive Sensing Technologies; Walking Speed

Indexed keywords

ARTIFICIAL INTELLIGENCE; COGNITIVE SYSTEMS; DECISION TREES; ENGINEERING EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; SIGNAL DETECTION;

MILD COGNITIVE IMPAIRMENTS; OLDER POPULATION; SENSING TECHNOLOGY; SMART SYSTEM; WALKING SPEED;

SIGNAL PROCESSING;

ACCURACY; AGED; ARTICLE; FEMALE; HUMAN; KERNEL METHOD; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MILD COGNITIVE IMPAIRMENT; PHYSICAL ACTIVITY; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINE; VERY ELDERLY; WALKING SPEED; ALGORITHM; ARTIFICIAL INTELLIGENCE; COGNITIVE DYSFUNCTION; DEVICES; PATHOPHYSIOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REMOTE SENSING;

AGED; AGED, 80 AND OVER; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COGNITIVE DYSFUNCTION; FEMALE; HUMANS; MALE; REMOTE SENSING TECHNOLOGY; ROC CURVE; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUPPORT VECTOR MACHINE;

EID: 84929077737     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2015.2389149     Document Type: Article

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