Dr Ya Huang
Dr Ya Huang specialises in human-motion dynamics and signal processing. He has been a Senior Lecturer in Engineering Dynamics at the University of Portsmouth since 2009. He leads research into 1) human responses to shock and vibration on cross transport platforms, representing UK experts view on ISO panels, and 2) 3D ocean wave imaging and modelling for vessel seakeeping, paving ways to human-centred autonomy of future land and marine transports. Before, he worked as a research fellow on high strain rate collapse of steel framed structures at the Blast and Impact Dynamics Group of Sheffield University. Ya obtained his doctorate degree in human responses to whole-body vibration in 2008 at the Institute of Sound and Vibration Research (ISVR), University of Southampton.
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 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. It 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 laboratory and sea trial studies of crew dynamic sitting and bracing strategies on fast lifeboats with the Royal National Lifeboat Institution, UK.
Ya's most recent research applies machine learning and vision algorithms to quantify human movement and 3D ocean wave reconstruction using stereo vision. These have led to recent investigation from musculoskeletal modelling of lifeboat crew for optimal bracing to the rebuilding and adaption of classic strip theory to interpret hydrodynamic loads on structure and crew. The research aims to develop computationally fast algorithms to predict vessel seakeeping performances in real-time, driving next generation human-centred autonomy of transports.