Victor Javier Kartsch Morinigo
Papers
2
Total Citations
34
H-Index
2
About
Victor Javier Kartsch Morinigo is a leading researcher at the intersection of embedded systems and biomedical engineering, specializing in energy-efficient, real-time human-machine interfaces. His primary contributions lie in advancing surface electromyography (sEMG) signal processing for intuitive robotic hand control, with a strong focus on deploying complex neural network models directly onto low-power edge microcontrollers. His most cited work, "sEMG-based Regression of Hand Kinematics with Temporal Convolutional Networks on a Low-Power Edge Microcontroller" (2021, 29 citations), pioneered the use of temporal convolutional networks for real-time, on-device hand kinematics regression, overcoming the computational constraints of portable systems. Building on this, his 2022 paper on sEMG neural spike reconstruction for gesture recognition demonstrated how to achieve high-accuracy gesture classification on multicore processors with minimal energy consumption. By bridging the gap between sophisticated deep learning algorithms and severely resource-limited hardware, Kartsch Morinigo’s research is pivotal for enabling next-generation wearable prosthetics and interactive devices that operate autonomously without cloud dependency. His work is widely cited for its practical approach to making advanced AI-driven control truly deployable in the real world.
Research Focus
Key Achievements
Top Papers
- 1
- 2