Kevin Englehart

About

Kevin Englehart is a leading figure in neural-machine interfaces and rehabilitation robotics, whose work has fundamentally advanced the control of artificial limbs. His major contributions center on decoding motor control information to create more intuitive prosthetics. His landmark 2007 study, with 216 citations, analyzed the novel "targeted muscle reinnervation" (TMR) technique, demonstrating how a neural-machine interface can dramatically improve function for amputees. Englehart also helped shape the field's direction, co-authoring the influential 2014 proceedings from the first workshop on Peripheral Machine Interfaces (214 citations), which critically examined the gap between decades of research and real-world prosthetic performance. Beyond biological interfaces, his innovative engineering is evident in the development of SHAPE TAPE™ (1999, 57 citations), a fiber optic curvature sensor providing six-degree-of-freedom position and orientation data. Most recently, his 2024 work on transfer learning for EMG and IMU-based force modeling (13 citations) tackles the crucial challenge of generalizing models across new users and conditions, promising to make assistive and rehabilitation devices more adaptable and effective for a broader population.

Research Focus

Key Achievements

Top Papers

1. 1
   Decoding a New Neural–Machine Interface for Control of Artificial Limbs

   216 citations · 2007
2. 2
   Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

   214 citations · 2014
3. 3
   Spatially continuous six degree of freedom position and orientation sensor

   57 citations · 1999
4. 4
   Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

   13 citations · 2024
5. 5
   <title>Spatially continuous six-degrees-of-freedom position and orientation sensor</title>

   13 citations · 1999

Key Collaborators

Contact & Links