The University of Southampton

Dr Shelly Vishwakarma PhD

Dr Shelly Vishwakarma is a Lecturer in Digital Health & Biomedical Engineering Group. Her current research focuses on designing and developing hardware and software frameworks to advance state-of-the-art opportunistic sensing using radio frequency (RF) signals from WiFi transmissions for contextual sensing applications, including concurrent physical activity recognition and indoor localization. Dr Vishwakarma received her PhD from the Indraprastha Institute of Information Technology, Delhi, India, in 2020, where her research investigated advanced signal processing techniques for human activity detection, classification, and imaging in indoor environments. Before joining ECS, she worked as a Research Fellow on an EPSRC-funded project, OPERA Opportunistic Passive RADAR for Non-Cooperative Contextual Sensing, at University College London. The OPERA project investigates a novel unobtrusive RADAR sensing technology for contextual sensing to facilitate healthcare and Ambient Assisted Living. During the global pandemic, she developed an animation data-driven human radio frequency (RF) scattering simulator, SimHumalator, to generate realistic RADAR signatures associated with activities relevant to healthcare, including sitting and standing to fall over. The simulator has been used across the globe to alleviate the well-known 'cold-start problem in RADAR, where there is a lack of useable data for training machine learning networks ( Dr Vishwakarma has won the best student paper award at IEEE International RADAR Conference, Atlanta, USA, 2020, and nomination for the best paper award at IEEE International RADAR Conference, Toulon, France, 2019. More recently, she won the second and third best paper awards in IEEE Radar Challenge, New York, 2022, for her work on developing an ML-assisted radar signal processing framework and building a hardware prototype using commercially available off-the-shelf components. 


Research interests

  • Digital Healthcare
  • Wireless sensing
  • Joint Communication and Sensing
  • AI-assisted Radar Technology
  • Simulation Modeling
  • Through-the-wall Sensing
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