Papers
7
Total Citations
63
H-Index
4
About
Triwiyanto Triwiyanto is a leading researcher at the intersection of biomedical engineering, rehabilitation robotics, and artificial intelligence. His work focuses on developing intelligent control systems for prosthetics and exoskeletons, primarily using electromyography (EMG) signals and machine learning. His most cited paper (30 citations) introduces a CNN-based method for predicting tool wear in belt grinding using force and vibration data, demonstrating his expertise in signal processing and deep learning applied to manufacturing. In rehabilitation, his 2023 study on using a single-lead EMG signal to control an upper limb exoskeleton via embedded machine learning on a Raspberry Pi (16 citations) showcases a practical, low-cost approach to assist post-stroke patients. Triwiyanto has also contributed to joint angle estimation using Kalman filtering and novel EMG features, and authored a comprehensive review on how deep learning can enhance prosthetics and exoskeletons. His work has been cited over 60 times, reflecting growing impact in assistive technology. By combining advanced neural networks with real-time biosignal processing, Triwiyanto is pushing the boundaries of accessible, intelligent rehabilitation devices.
Research Focus
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- 7A Review on Robotic Hand Exoskeleton Devices: State-of-the-Art Method2 citations · 2021