Dr Ya Huang specialises in human-motion dynamics and signal processing. He has been a Senior Lecturer in Engineering Dynamics at Portsmouth since 2009. Before that, he worked as a post-doctorate researcher on high strain rate collapse of steel framed structures at the Blast and Impact Dynamics Group at the University of Sheffield. His doctorate training in human responses to whole-body vibration concluded in 2008 at the Institute of Sound and Vibration Research (ISVR), University of Southampton.

Research interests

Through understanding force and motion experienced by the human body i.e. biomechanics, Dr Huang’s research interest has been to apply analytical and computational methods to understand human responses to whole-body vibration and repeated shocks experienced in different modes of transport on land, in the air and at sea. He has over 20-year research experience in the laboratory and in the field.


Dr Huang has contributed to new methodologies to reconstruct multi-channel nonlinear and cross-correlated force and motion signals during whole-body vibration. They serves as a key step for modelling ride quality and injury prevention on different transport platforms. These outcomes reduce the computational costs and enhance motion transmission models leading to better design and assessment approaches. He has led experimental studies of crew dynamic sitting and bracing strategies on fast lifeboats funded by the Royal National Lifeboat Institution (RNLI) UK. 


Ya leads a new research to advance visual computing algorithms to quantify fast vessel seakeeping performances via 3D a) ocean wave reconstruction using stereo vision, b) vessel hydrodynamic and c) human biomechanical models. These have been realised by his recent investigations from musculoskeletal modelling of lifeboat crew for optimal bracing, and adaption of wave stereo vision systems, to the complete rebuilding and adaption of classic boundary element method to interpret hydrodynamic loading on the vessel and then the crew. Funded by the EPSRC (EP/X035778/1), this new research aims to develop computationally fast algorithms to predict vessel seakeeping performances in real-time, enhancing maritime safety and driving for next generation human-centred autonomy of marine transports.