Muskan Dawar
Papers
1
Total Citations
4
H-Index
1
About
Muskan Dawar is a researcher at the intersection of machine learning and e-health, with a focus on developing accessible, simulation-guided tools for biomedical applications. Her most-cited work, "An OpenSim guided tour in machine learning for e-health applications" (2020), has garnered 4 citations and exemplifies her commitment to bridging computational modeling with practical healthcare solutions. By leveraging OpenSim—an open-source biomechanics simulation platform—Dawar demonstrates how machine learning can be integrated into e-health workflows, enabling more accurate analysis of human movement and physiological data. This contribution is particularly valuable for researchers and clinicians seeking to adopt AI-driven approaches without requiring deep technical expertise. Dawar’s work underscores the potential of combining simulation environments with predictive algorithms to advance personalized medicine and rehabilitation technologies. While her citation count is modest, her research lays foundational groundwork for scalable, user-friendly e-health systems. As the field of digital health continues to expand, Dawar’s efforts to democratize machine learning tools for biomedical applications position her as a promising voice in the ongoing dialogue between computational science and patient-centered care.
Research Focus
Key Achievements
Top Papers
- 1An OpenSim guided tour in machine learning for e-health applications4 citations · 2020