Honors Fellowship Summary Post

Well, everyone…our seldom productive summers have already ended, and it is time to step up and start writing our honors papers! This season, I learned so much more than my computer science grades indicate. My Python skills are continuous (i.e., nearly 2 years of experience already), and one of my academic interests in artificial intelligence comes to life. Since December 2018, I have made significant progress from the search for the perfect honors advisor to midterm data collection and analysis. In this post, I am going to provide a revised abstract of my honors project with emphasis on my summer work.

My Honors Abstract Effective September 2019

According to the World Health Organization, falls are the second-leading cause of accidental death, especially among senior adults around the world. Existing technologies, including bedside sensors, can help minimize such falls and characterize walking and other movements. However, they fail to provide non-intrusive measurements serving as benchmarks for a patient who is likely to fall again. Currently, a field of science called computer vision, which enables computers to see and process digital images and videos, appears to make sense of the data that focuses on falls detection. The purpose of this honors project is to utilize OpenCV-Python code to analyze images and videos of the falls risk step assessments that Williamsburg Landing residents took in the previous year. In particular, the code emphasizes examining the properties of blob detection and analyzing its behavior when other OpenCV-Python methods apply to falls prevention testing videos. To date, background subtraction and contrast enhancement have eased the process of tracking desired blobs on a patient for the duration of one of those videos. Likewise, various image processing histograms demonstrate a range of frequencies of grayscale pixel values, thus encouraging a more in-depth focus on extracting simpler falls risk testing measurements from the blobs. The outcome of this project is that blob detection software will be among other novel technologies that fall prevention investigators can use to process and provide relevant information on falls risk assessment results that should be more thorough than in the previous years. 


What’s Next?

I am going to make blogging my least favorite part of the fellow experience (for now). My thesis is highly computational, and it demands a lot of time as I analyze and correct algorithms. As a result, all I would care about is writing the thesis and other required reports themselves. I also am looking forward to starting my literature review and presenting my work at the Spring Undergraduate Research Symposium and other conferences near and far. Thank you to everyone who has helped me realize my strengths. Now, I am going to sign off until further notice and comment on other fellows’ projects. Until next time!

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