Evan Campbell
Papers
3
Total Citations
20
H-Index
2
About
Evan Campbell is a rising researcher in human–machine interaction, with a focus on myoelectric control and wearable sensing. His work addresses a critical barrier to the real-world adoption of electromyography (EMG)-based interfaces: the inability of models to generalize across users and conditions, and the persistent problem of false activations during daily activities. Campbell’s research introduces novel paradigms such as “wake gestures” and on-demand myoelectric control, which allow systems to remain idle until intentionally triggered, dramatically reducing unintended commands. His 2024 paper on transfer learning for upper limb force modeling, which integrates EMG and IMU data, has already garnered 13 citations, signaling its impact on the field. By tackling both model generalization and real-world reliability, Campbell is helping to pave the way for more practical, hands-free control of assistive, rehabilitative, and robotic devices. His work stands out for its direct relevance to everyday usability, making him a key voice in the next generation of myoelectric interface design.
Research Focus
Key Achievements
Top Papers
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