Papers
8
Total Citations
305
H-Index
6
About
Markus Nowak is a researcher whose work sits at the intersection of assistive robotics, machine learning, and human-machine interfaces, with a particular focus on myoelectric control of prosthetic hands. His most influential contribution, "Stable myoelectric control of a hand prosthesis using non-linear incremental learning" (2014, 139 citations), demonstrated how machine learning — specifically incremental learning approaches — could dramatically improve the stability and dexterity of prosthetic limb control using surface electromyography (sEMG). This foundational work helped shift the field toward more adaptive, data-driven control paradigms capable of handling multiple degrees of freedom simultaneously. Building on this, Nowak developed low-cost wearable multichannel sEMG hardware (71 citations) and explored simultaneous, proportional bimanual control (45 citations), pushing myocontrol toward real-world clinical usability. His later research expanded into multi-modal sensing by combining force and electromyography signals and investigating automated instability detection to enhance prosthesis reliability. Perhaps most distinctively, Nowak bridged engineering and epistemology by applying radical constructivism to interactive machine learning, offering a novel theoretical lens for human-centered AI in rehabilitation robotics. With over 300 cumulative citations, his body of work represents a meaningful and continuing contribution to upper-limb prosthetics research.
Research Focus
Key Achievements
Top Papers
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- 4Multi-modal myocontrol: Testing combined force- and electromyography24 citations · 2017
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- 6Wrist and grasp myocontrol: Online validation in a goal-reaching task6 citations · 2016
- 7Wrist and grasp myocontrol: Simplifying the training phase2 citations · 2015
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