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Keshav Anand
2025-12-08 22:19:11 -06:00
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\newcommand{\mytext}{Firstly, I find Computer Science deeply intriguing as it is the perfect crossover between my two passions of \newcommand{\mytext}{During the antisocial years of the COVID-19 pandemic, my endless YouTube bingeing
mathematics and problem-solving. The ability to optimize a computer chip to solve a pratical problem has led me into
always fascinated me, and the rising popularity of Machine Learning and Artificial Intelligence further accidentally discovering how computers work at a low level. The moment I learned the fascinating
piqued my interest during the COVID-19 pandemic. My research project from 2024 - 2025, GaitGuardian, started my journey marvel of engineering that is a computer, I immediately fell in love with Computer Science.
with signal processing, as I had worked on a project to predict Freezing of Gait (FoG) episodes in Parkinson's Over the years, I have also developed a specialized interest in signal processing and its applications.
Disease patients using a belt-mounted IMU sensor and machine learning algorithms. In addition to building My curiousity in this topic stems from my first Math Club meeting in 9th grade, where a senior officer
an end-to-end hardware embedding with a custom PCB, I also developed a strong understanding of signal processing had explained the fascinating science of how radio signals are transmitted and received using Fourier transforms
techniques. My novel pipeline involved using fourier transforms, z-score normalization, and wavelet denoising (as an application of calulus).
These concepts overlapped with my learning of fascinating Calculus concepts, and I was amazed at how signals can
be analyzed and processed. Today, the major question that excites me within the field of Signal Processing is how to effectively
adapt digital signal processing and machine learning for real-time resource-constrained embedded systems.
As a hardcore robotics enthusiast, I have always been not just interested in the theoretical software,
but also the practical hardware embedding of these algorithms. This really sparked my interest in the field
of signal processing for embedded systems, prompting me to start a 2 year research project that would become the
focus of my life.
My ISEF-winning research project from 2024 - 2025, GaitGuardian, was my first major experience with signal processing,
as I had worked on a project to predict Freezing of Gait (FoG) episodes in Parkinson's
Disease patients using a belt-mounted IMU sensor and machine learning algorithms. My novel pipeline involved using fourier transforms, z-score normalization, and wavelet denoising
to filter out noise from the raw IMU data. Unlike existing approachs that used time-domain features, I fed the to filter out noise from the raw IMU data. Unlike existing approachs that used time-domain features, I fed the
cleaned time-series data into a 1D CNN, acting as an automatic feature extractor (with no flattening). cleaned time-series data into a 1D CNN, acting as an automatic feature extractor (with no flattening).
This was passed into a hybrid biLISTM with temporal and spatial attention mechanisms, This was passed into a hybrid biLISTM with temporal and spatial attention mechanisms,
allowing for segmented windows to be read both forwards and backwards, and a final dense layer would allowing for segmented windows to be read both forwards and backwards, and a final dense layer would
output the boolean state of whether a FoG episode was occuring in real time. The learning I gained from this research led output the boolean state of whether a FoG episode was occuring in real time. The learning I gained from this research led
me to pursue other related signal processing tasks to boost the final product for Parkinson's patients. me to pursue other related signal processing tasks to boost the final product for Parkinson's patients.
Researching other Parkinsonian symptoms led me to explore tremor detection (uncontroleld shaking of the hands), Researching other Parkinsonian symptoms led me to explore tremor detection (uncontrolled shaking of the hands),
and I implemented a real-time tremor detection model that involved a bandpass butterworth filter to isolate tremor frequencies and I implemented a real-time tremor detection model that involved a bandpass butterworth filter to isolate tremor frequencies
between 4-6 Hz, followed by an FFT to extract frequency-domain features. These features were then fed into a lightweight between 4-6 Hz, followed by an FFT to extract frequency-domain features. These features were then fed into a lightweight
1D CNN, resulting in state-of-the-art 99\% accuracy while limiting false positives. I also looked into signal processing 1D CNN, resulting in state-of-the-art 99\% accuracy while limiting false positives. I also looked into signal processing