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Predicting If Older Adults Perform Cognitive Tasks Using Body Joint Movements From RGB-D Videos

Authors:
Jenna Ryan
Sarah Inzerillo
Jordan Helmick
Ali Boolani
Natasha Kholgade Banerjee
Sean Banerjee

Keywords: RGB-D; Kinect; cognitive; fatigue; random forest

Abstract:
We present an approach that performs automated detection of whether an older adult has performed cognitive tasks such as form filling or problem solving using RGB-D video data of older adults collected using the Microsoft Kinect v2 sensor. Our approach uses the variances of 25 joint points on the 3D skeleton obtained from the Kinect for training random forest classifiers to detect if cognitive tasks are performed, based on deviations in postural sway induced by cognitive tasks. We validate our approach using a dataset of 10 subjects performing the test on standing with eyes closed in the Berg Balance Scale (BBS) series of diagnostic tests before and after cognitive tasks. Using leave-one-subject-out cross-validation, we obtain an average detection accuracy of 69.5%, with accuracies of 60% and 79% at detecting that the test on standing with eyes closed was performed prior to and after cognitive tasks respectively. Our approach can be incorporated into intelligent health care systems to detect whether older adults have performed cognitively demanding activities that may induce stress or fatigue, and allow early intervention well before the occurrence of adverse events such as falls.

Pages: 102 to 107

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2308-4359

ISBN: 978-1-61208-688-0

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019