Faruq Sandi Hanggara

About

Faruq Sandi Hanggara is a researcher advancing the frontiers of assistive robotics and human-machine interaction, with a primary focus on decoding human motor intent. His work centers on two key areas: electromyography (EMG)-based finger movement classification and brain-computer interfaces (BCI) for prosthetic control. In his most-cited paper, "Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband" (2023, 4 citations), Hanggara systematically evaluated machine learning approaches for interpreting EMG signals, a critical step toward enabling intuitive control of exoskeletons, prosthetic hands, and powered wheelchairs. His earlier work, "Brain-Computer Interface based on Neural Network with Dynamically Evolved for Hand Movement Classification" (2022, 2 citations), tackled the challenge of translating neural commands into prosthetic movements using adaptive neural networks. Together, these contributions address a fundamental hurdle in assistive technology: creating control systems that feel like natural extensions of the human body. Hanggara’s research directly impacts the development of more responsive, user-friendly devices for individuals with disabilities, bridging the gap between biological signals and robotic action.

Research Focus

Key Achievements

Top Papers

1. 1
   Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

   4 citations · 2023
2. 2
   Brain-Computer Interface based on Neural Network with Dynamically Evolved for Hand Movement Classification

   2 citations · 2022

Key Collaborators

Contact & Links