Dwi Esti Kusumandari

About

Dwi Esti Kusumandari is a researcher at the forefront of medical robotics and intelligent health monitoring systems. Her work primarily focuses on integrating computer vision and machine learning to enhance the safety and precision of medical robots. A key contribution is her development of a non-invasive blood pressure prediction method using Photoplethysmograph (PPG) signals. By applying Support Vector Regression (SVR) to data from 110 participants, her 2024 study achieved notable accuracy, paving the way for more sophisticated, touchless patient monitoring in robotic surgery. Complementing this, her 2022 research on face liveness detection using Convolutional Neural Networks (CNNs) addresses a critical security challenge: ensuring that a medical robot’s facial verification system cannot be spoofed by a photograph. This work is essential for safe human-robot interaction in Society 5.0 applications. While her citation counts are early indicators of a growing impact, Kusumandari’s research is strategically positioned at the intersection of patient safety, biometric security, and intelligent automation, making her a promising voice in the evolution of smart healthcare robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Precision blood pressure prediction leveraging Photoplethysmograph signals using Support Vector Regression

   8 citations · 2024
2. 2
   Face Liveness Detection Using CNN for Face Verification on Medical Robot

   2 citations · 2022

Key Collaborators

Contact & Links