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You searched for +publisher:"Texas A&M University – Corpus Christi" +contributor:("Sefcik, Elizabeth"). Showing records 1 – 2 of 2 total matches.

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1. Alkittawi, Hend. A deep-learning-based fall-detection system to support aging-in-place .

Degree: 2017, Texas A&M University – Corpus Christi

Emergency departments treat around 2.5 million older people for fall injuries each year. Serious head and broken bones injuries occur in 20% of falls. Fall injuries, adjusted for inflation, has direct medical costs of $34 billion a year. Taking into account that people 65 and older are expected to comprise 21.7% of the U.S population in 2040, compared to 14.4% in year 2013, the numbers presented in the statistics will dramatically increase as well. Preserving the elderlys' right of aging in a home of their own choice is mandatory in today's world, as more elderly people are willing to live independently. But, with the statistics showing that falling is a major health problem that has a huge non-desirable impact on elderly lives, fall detection systems become a necessity. Different approaches have been used to design fall detection systems. One approach depends on wearable sensors that measure different physical parameters of a human body or the environment around it, such as the body acceleration or its pressure on the floor. A second approach depends on sensors employed in the environment. These sensors mainly include wide-angle cameras, depth cameras, and microphones. Different approaches used different classifiers for training the system to detect falls. Despite these efforts to detect falls, it is possible that other naturally occurring falls trigger false alarms. Thus, the current implementations of fall detection systems need to be improved. Most recently, computer vision based approaches using depth cameras are the mostly used for such improvement. Using deep neural networks to learn features from video frames have a potential to improve the fall-detection accuracy and reduce triggering false alarms. In this study, a more robust and deep fall-detection system was designed. This approach extends deep convolutional neural networks in time. This extension allows capturing the spatial and temporal information presented through successive video frames. The result of the new approach can be used to implement a reliable surveillance system in a real-world environment. Advisors/Committee Members: Rahnemoonfar, Maryam (advisor), Mahdy, Ahmed (committeeMember), Sefcik, Elizabeth (committeeMember).

Subjects/Keywords: deep-learning; fall-detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alkittawi, H. (2017). A deep-learning-based fall-detection system to support aging-in-place . (Thesis). Texas A&M University – Corpus Christi. Retrieved from http://hdl.handle.net/1969.6/2975

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alkittawi, Hend. “A deep-learning-based fall-detection system to support aging-in-place .” 2017. Thesis, Texas A&M University – Corpus Christi. Accessed July 21, 2019. http://hdl.handle.net/1969.6/2975.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alkittawi, Hend. “A deep-learning-based fall-detection system to support aging-in-place .” 2017. Web. 21 Jul 2019.

Vancouver:

Alkittawi H. A deep-learning-based fall-detection system to support aging-in-place . [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2017. [cited 2019 Jul 21]. Available from: http://hdl.handle.net/1969.6/2975.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alkittawi H. A deep-learning-based fall-detection system to support aging-in-place . [Thesis]. Texas A&M University – Corpus Christi; 2017. Available from: http://hdl.handle.net/1969.6/2975

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Callender, Karisse A. Implications for professional quality of life and wellness-based supervision .

Degree: 2017, Texas A&M University – Corpus Christi

Supervision of counselors-in-training (CITs) is the signature pedagogy within counselor preparation programs that moderates and regulates personal and professional development. There are few empirical studies demonstrating causal relationships between wellness-based supervision and the effects on CITs professional quality of life. This dissertation evaluated the efficacy of the Wellness Model of Supervision (WELMS; Lenz & Smith, 2010) for promoting desired change across CITs perception of their professional quality of life across three variables: secondary traumatic stress, compassion satisfaction, and burnout. A small series A-B single-case research design (SCRD) with multiple baselines was implemented to evaluate the degree of efficacy for the WELMS to promote professional quality of life characteristics among CITs over time. This design was selected based on practicality for estimating functional relationships associated with an intervention and target outcomes. Three women who identified as Caucasian (n = 1) and Hispanic (n = 2) and were enrolled in Internship at a Council for Accreditation of Counseling and Related Educational Programs (CACREP) accredited program participated in the study. Results indicated that the WELMS may be efficacious across client-CIT interactions, and CITs who receive the WELMS are likely to show improvement in the secondary traumatic stress and burnout subscales of the ProQOL. These results are consistent with previous studies utilizing the WELMS with CITs and provided several recommendations for future researchers regarding implementing the WELMS while considering unique student characteristics. Wellness-based approaches within supervisory relationships can inspire CITs to improve their personal wellness and professional quality of life while enhancing skill development and career sustaining behaviors. Advisors/Committee Members: Lenz, A. Stephen (advisor), Watson, Joshua C (committeeMember), Ricard, Richard J (committeeMember), Sefcik, Elizabeth (committeeMember).

Subjects/Keywords: Professional Quality of Life; Single-Case Research Design; Supervision; Wellness

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Callender, K. A. (2017). Implications for professional quality of life and wellness-based supervision . (Thesis). Texas A&M University – Corpus Christi. Retrieved from http://hdl.handle.net/1969.6/5624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Callender, Karisse A. “Implications for professional quality of life and wellness-based supervision .” 2017. Thesis, Texas A&M University – Corpus Christi. Accessed July 21, 2019. http://hdl.handle.net/1969.6/5624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Callender, Karisse A. “Implications for professional quality of life and wellness-based supervision .” 2017. Web. 21 Jul 2019.

Vancouver:

Callender KA. Implications for professional quality of life and wellness-based supervision . [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2017. [cited 2019 Jul 21]. Available from: http://hdl.handle.net/1969.6/5624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Callender KA. Implications for professional quality of life and wellness-based supervision . [Thesis]. Texas A&M University – Corpus Christi; 2017. Available from: http://hdl.handle.net/1969.6/5624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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