ABSTRACT
Parkinson’s disease and other neurological disorders often manifest
with involuntary tremors, significantly impacting quality of life.
This study proposes the Modified Kalman Filter for tremor attenuation,
proposing a novel modification to enhance its performance in
hand vibration control. Despite the advancements in adaptive algorithms
and deep learning models for tremor mitigation, challenges
such as data acquisition and implementation in wearable devices
persist. By leveraging the Kalman Filter’s ability to estimate system
states, we aim to develop a more effective and accessible solution
for individuals with tremors. Our findings demonstrate that the
Modified Kalman Filter can achieve significant tremor reduction,
when compared to Kalman Filter, paving the way for future research
in wearable assistive technologies and brain-computer interfaces.