Shradha Singhvi
Papers
2
Total Citations
12
H-Index
2
About
Shradha Singhvi is a researcher at the intersection of biomedical engineering and human-robot interaction, with a primary focus on wearable robotics and surgical navigation. Her work centers on two key areas: electromyography (EMG)-based gesture recognition for assistive devices, and intra-operative image guidance to improve surgical precision. In her most cited work (9 citations), Singhvi developed a comparative framework for motion recognition using temporal modeling of EMG signals from thumb and index finger movements. By evaluating time-domain features and machine learning classifiers, she demonstrated how surface EMG from a novel wearable device can reliably decode fine motor gestures—a critical step toward intuitive control of robotic finger exoskeletons for rehabilitation. Her subsequent research (3 citations) tackled the challenge of morphological deformation in image-guided surgery, proposing a method that integrates 3D surface scanning to update navigation models in real time. This work addresses a persistent limitation in procedures where soft tissue shifts compromise accuracy. Singhvi’s contributions bridge the gap between sensing and actuation in wearable systems, offering practical pathways for adaptive, patient-specific robotic assistance. Her interdisciplinary approach—combining signal processing, machine learning, and clinical application—positions her work as a foundation for next-generation, user-responsive rehabilitation technologies.
Research Focus
Key Achievements
Top Papers
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- 2