Mohammad Taufik

About

Mohammad Taufik is a researcher at the forefront of integrating artificial intelligence with healthcare and assistive robotics. His work primarily spans biomedical signal processing, human-robot interaction, and machine learning applications. Taufik’s most notable contribution is the development of a non-invasive blood pressure prediction system using Photoplethysmograph (PPG) signals from a single finger. By applying Support Vector Regression (SVR), his 2024 study achieved precise blood pressure estimation from 110 participants aged 20 to 70, a breakthrough that could enable smarter medical robots and continuous health monitoring without cumbersome cuffs. This work has already garnered 8 citations, reflecting its growing influence. In parallel, Taufik advanced socially assistive robotics by designing a prototype that combines object detection and face recognition via Convolutional Neural Networks (CNNs). This system, published in 2021, allows robots to interact socially and assist in parental monitoring, demonstrating how AI can foster meaningful human-robot relationships. With 5 citations, this research underscores his commitment to creating empathetic, intelligent machines. Taufik’s work bridges the gap between precise biomedical tools and socially aware robotics, positioning him as a key innovator in AI-driven healthcare and assistive technologies.

Research Focus

Key Achievements

Top Papers

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

   8 citations · 2024
2. 2
   Socially Assistive Robot Interaction by Objects Detection and Face Recognition on Convolutional Neural Network for Parental Monitoring

   5 citations · 2021

Key Collaborators

Contact & Links