Sivanagaraja Tatinati
Kyungpook National University, Nanyang Technological University
Papers
12
Total Citations
268
H-Index
8
About
Sivanagaraja Tatinati is a researcher whose work sits at the intersection of biomedical signal processing, machine learning, and surgical robotics, with a particular focus on one of microsurgery's most persistent challenges: physiological tremor compensation. His research has made significant strides in developing algorithms that accurately filter and predict involuntary hand tremors in real time, enabling handheld robotic surgical instruments to perform with greater precision and reliability. Tatinati's most influential contribution, "Multistep Prediction of Physiological Tremor Based on Machine Learning for Robotics Assisted Microsurgery" (2014, 75 citations), demonstrated how machine learning could overcome the critical problem of phase delay introduced by hardware sensors and software filters in robotic devices. Complementary works employing autoregressive models, Kalman filters, and least-squares support vector machines further established his expertise in adaptive signal processing for real-time surgical applications. His 2020 paper revisited phase distortion in tremor filtering, proposing solutions to a long-standing limitation in the field. Beyond microsurgery, Tatinati extended his predictive modeling expertise to radiotherapy, developing ensemble learning methods for respiratory motion prediction in robotic radiosurgery. With over 260 cumulative citations, his body of work represents a meaningful contribution to making robotic-assisted surgery safer and more precise for patients worldwide.
Research Focus
Key Achievements
Top Papers
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- 9Multi-step prediction of physiological tremor for robotics applications8 citations · 2013
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