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Keshav Anand
2025-12-08 19:26:03 -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}{Firstly, I find Computer Science deeply intriguing as it is the perfect crossover between my two passions of
mathematics and problem-solving. The ability to optimize a computer chip to solve a pratical problem has mathematics and problem-solving. The ability to optimize a computer chip to solve a pratical problem has
always fascinated me, and the rising popularity of Machine Learning and Artificial Intelligence further always fascinated me, and the rising popularity of Machine Learning and Artificial Intelligence further
piqued my interest during the COVID-19 pandemic. My research project from 2024 - 2025 started my journey piqued my interest during the COVID-19 pandemic. My research project from 2024 - 2025, GaitGuardian, started my journey
with signal processing, as I had worked on a project to predict Freezing of Gait (FoG) episodes in Parkinson's 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. In addition to building Disease patients using a belt-mounted IMU sensor and machine learning algorithms. In addition to building
an end-to-end hardware embedding with a custom PCB, I also developed a strong understanding of signal processing an end-to-end hardware embedding with a custom PCB, I also developed a strong understanding of signal processing
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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.} 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.
Researching other Parkinsonian symptoms led me to explore tremor detection (uncontroleld shaking of the hands),
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
1D CNN, resulting in state-of-the-art 99\% accuracy while limiting false positives. I also looked into signal processing
within my FTC robotics team, realizing that IMU data could be used to improve odometry and localization. I implemented a
custom Kalman filter to fuse IMU data with wheel encoder reading, significantly reducing drift during autonomous navigation
and reducing error buildup over time. }