Jacob Tryon

Papers

Total Citations

H-Index

About

Jacob Tryon is a biomedical engineer and researcher specializing in human-machine interfaces, neural signal processing, and wearable robotic systems. His work centers on developing reliable and intuitive control methods for exoskeletons and assistive devices, with a particular focus on fusing electroencephalography (EEG) and electromyography (EMG) signals to accurately classify user motion intent. Tryon's 2019 paper evaluating EEG/EMG fusion methods for motion classification has garnered 28 citations, establishing him as an early contributor to this emerging field. Building on this foundation, his 2021 investigation into convolutional neural networks as a fusion methodology — cited 20 times — demonstrated the power of deep learning approaches in decoding complex biosignals for device control. His more recent 2025 work examining the effects of image normalization on CNN-based fusion reflects his continued refinement of these techniques. Collectively, Tryon's research addresses a critical barrier to the widespread adoption of wearable robotic exoskeletons: the gap between human intent and device response. His contributions offer meaningful progress toward rehabilitation technologies that feel natural and responsive for patients with musculoskeletal and mobility disorders.