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Activity analysis and detection of falling and repetitive motion

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Date Issued:
2013
Summary:
This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
Title: Activity analysis and detection of falling and repetitive motion.
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Name(s): Carryl, Clyde
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: multipart monograph
Date Issued: 2013
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: xi, 88 p. : ill. (some col.)
Language(s): English
Summary: This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
Identifier: 849650312 (oclc), 3360774 (digitool), FADT3360774 (IID), fau:4091 (fedora)
Note(s): by Clyde Carryl.
Thesis (M.S.C.S.)--Florida Atlantic University, 2013.
Includes bibliography.
Mode of access: World Wide Web.
System requirements: Adobe Reader.
Subject(s): Perpetual-motion processes
Human locomotion
Neural networks (Computer science)
Artificial intelligence
Persistent Link to This Record: http://purl.flvc.org/FAU/3360774
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Owner Institution: FAU