Date of Award
2013
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Abstract
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.
Extent
xi, 88 p. : ill. (some col.)
Recommended Citation
Carryl, Clyde, "Activity analysis and detection of falling and repetitive motion" (2013). Electronic Theses and Dissertations 2010 - 2019. 683.
https://digitalcommons.fau.edu/etd_general_6/683
Identifier
FADT3360774
Comments
by Clyde Carryl.|Thesis (M.S.C.S.)--Florida Atlantic University, 2013.|Includes bibliography.|Mode of access: World Wide Web.|System requirements: Adobe Reader.|by Clyde Carryl.