Vennila Krishnan
Papers
2
Total Citations
10
H-Index
2
About
Vennila Krishnan is a pioneering researcher at the intersection of rehabilitation engineering and surgical skill assessment. Her work primarily focuses on leveraging sensor-based technologies and machine learning to quantify human motor performance in clinical and surgical settings. In a landmark 2016 study, Krishnan demonstrated that robotic-assisted locomotor training significantly enhances ankle joint control in individuals with chronic incomplete spinal cord injury, providing crucial evidence for the role of robotics in neurorehabilitation. Her more recent work (2025) pushes the boundaries of surgical education by developing explainable machine learning models that use EMG and accelerometer data to objectively quantify surgical expertise and identify biomarkers of proficiency. This approach addresses the critical need for scalable, interpretable tools to replace subjective evaluations in robotic and simulation-based training. With her research accumulating citations across rehabilitation and surgical domains, Krishnan is establishing herself as a key figure in the translation of wearable sensor analytics into practical clinical and educational applications, bridging the gap between human movement science and artificial intelligence.
Research Focus
Key Achievements
Top Papers
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