MohammadAli Shaeri
Papers
1
Total Citations
4
H-Index
1
About
MohammadAli Shaeri is a rising researcher at the forefront of neural engineering and prosthetic control, with a focus on creating next-generation hand prostheses. His most-cited work, "A Hardware-Efficient EMG Decoder with an Attractor-based Neural Network for Next-Generation Hand Prostheses" (2024), addresses a critical bottleneck in robotic prosthetic hands: the gap between advanced machine learning algorithms and practical, real-time hardware implementation. Shaeri’s key contribution is the development of a novel attractor-based neural network decoder that efficiently processes electromyographic (EMG) signals to enable precise, multi-finger movement decoding—moving beyond the basic on/off commands that limit current commercial prosthetics. By prioritizing hardware efficiency, his approach paves the way for more intuitive and dexterous control in portable devices. With 4 citations already for this recent publication, his work is gaining traction among engineers and clinicians seeking to translate high-degree-of-freedom control from lab settings to real-world use. Shaeri’s research sits at the intersection of embedded systems, machine learning, and biomedical engineering, promising to restore not just function but nuanced hand movement for amputees.
Research Focus
Key Achievements
Top Papers
- 1