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A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction

Angeleas, Anargyros
http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

Abstract Details

2018, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
Human Activity Recognition is an actively researched domain for the past few decades, and is one of the most eminent applications of today. It is already part of our life, but due to high level of uncertainty and challenges of human detection, we have only application specific solutions. Thus, the problem being very demanding and still remains unsolved. Within this PhD we delve into the problem, and approach it from a variety of viewpoints. At start, we present and evaluate different architectures and frameworks for activity recognition. Henceforward, the focal point of our attention is automatic human activity recognition. We conducted and present a survey that compares, categorizes, and evaluates research surveys and reviews into four categories. Then a novel fully automatic view-independent multi-formal languages collaborative scheme is presented for complex activity and emotion recognition, which is the main contribution of this dissertation. We propose a collaborative three formal-languages, that is responsible for parsing manipulating, and understanding all the data needed. Artificial Neural Networks are used to classify an action primitive (simple activity), as well as to define change of activity. Finally, we capitalize the advantages of Fuzzy Cognitive Maps, and Rule-Based Colored Petri-Nets to be able to classify a sequence of activities as normal or ab-normal.
Nikolaos Bourbakis, Ph.D. (Advisor)
Soon Chung, Ph.D. (Committee Member)
Mateen Rizki, Ph.D. (Committee Member)
George Tsihrintzis, Ph.D. (Committee Member)
265 p.
Computer Science
Human Activity Recognition

Recommended Citations

Citations

  • Angeleas, A. (2018). A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

    APA Style (7th edition)

  • Angeleas, Anargyros. A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction. 2018. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238.

    MLA Style (8th edition)

  • Angeleas, Anargyros. "A Multi-Formal Languages Collaborative Scheme for Complex Human Activity Recognition and Behavioral Patterns Extraction." Doctoral dissertation, Wright State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1526984767684238

    Chicago Manual of Style (17th edition)

wright1526984767684238
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© 2018, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.