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Activity analysis and detection of falling and repetitive motion
- 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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51 downloads |
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Name(s): |
Carryl, Clyde College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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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. |
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Subject(s): |
Perpetual-motion processes Human locomotion Neural networks (Computer science) Artificial intelligence |
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Persistent Link to This Record: | http://purl.flvc.org/FAU/3360774 | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU |