Andreas Ejupi
Papers
2
Total Citations
49
H-Index
2
About
Andreas Ejupi is a researcher at the forefront of wearable technology and human movement analysis, with a focus on developing intelligent systems for health monitoring and rehabilitation. His work centers on the innovative use of textile-based stretch sensors combined with machine learning to capture and interpret human kinematics. In his most-cited study, "Estimation of Knee Joint Angle Using a Fabric-Based Strain Sensor and Machine Learning: A Preliminary Investigation" (43 citations), Ejupi demonstrated a novel approach for in-home rehabilitation and long-term tracking of knee disorders, showcasing the potential of soft, wearable sensors to replace traditional rigid devices. His exploratory work, "Quantification of Textile-Based Stretch Sensors Using Machine Learning" (6 citations), further addresses the challenge of non-linear sensor properties, paving the way for more accurate applications in robotics, virtual reality, and healthcare. By bridging the gap between flexible materials and data-driven algorithms, Ejupi’s contributions are instrumental in advancing accessible, continuous monitoring systems that empower patients and clinicians alike. His research not only highlights the feasibility of smart textiles but also sets a foundation for future innovations in personalized, at-home health technologies.
Research Focus
Key Achievements
Top Papers
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