Matthew Guess
Papers
1
Total Citations
5
H-Index
1
About
Dr. Matthew Guess is a rising researcher at the intersection of biomechanics and machine learning, with a primary focus on developing non-invasive methods to assess and treat musculoskeletal disorders. His most cited work, "Machine learning full 3-D lower-body kinematics and kinetics on patients with osteoarthritis from electromyography" (2023, 5 citations), pioneers the use of EMG signals to predict complex lower-body movement patterns in osteoarthritis patients. This contribution is significant because it offers a practical, sensor-based alternative to expensive motion-capture systems, potentially enabling more accessible gait analysis in clinical settings. By demonstrating that machine learning can accurately reconstruct 3-D joint angles and forces from surface EMG, Dr. Guess has opened new pathways for remote monitoring and personalized rehabilitation. His work directly addresses the needs of millions suffering from osteoarthritis, aiming to improve quality of life through better diagnostic tools. Though early in his career, his innovative fusion of AI with biomechanical modeling marks him as a promising contributor to the future of digital health and assistive technology.
Research Focus
Key Achievements
Top Papers
- 1