Messing With Machine Learning

Hey everyone! Sorry for the delay in my honors blogging experience. I was trying to get as much methodology as possible, and it was worth the four weeks of work-relax balance after arriving at the College.

First Experiment: OpenCV-Python for Signal Processing and Feature Detection

So far, I am reading and analyzing videos and images of patients taking gait exams at Williamsburg Landing. At the start of the fellowship, my honors advisor introduced me to computer vision software called OpenCV-Python, which is an open source software that uses Python to solve computer vision problems and combines the aspects of both the Python language and the OpenCV C++ API. With pieces of code that can detect circles and edges and analyze moving subjects, OpenCV may be useful for making simple measurements for a patient with black circles on their ankles, legs, and other appropriate parts of the body for gait analysis. The languages it provides are C++, Java, and Python, with Python that I am using as the standard language throughout part of the honors project.

Latest Motivations 

Two weeks ago, I met another student who planned on making wearable sensors for preventing falls among seniors, with his approach to light sensors and such hardware as Raspberry Pi. I find his project as an interesting way to move forward. Even my advisor mentioned that I must show independent development of a sensor that can detect measurements located throughout the patient’s body and convert them into simple numerics. I hoped to be making good progress on that using Python in the context of machine learning. I am still hoping to finally practice my electronics skills in time for the engineering design capstone, where an interdisciplinary student team might choose to build the falls prevention sensor prototypes with a sea of Arduinos, breadboards, and Raspberry Pies.

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