Publications by authors named "Hancong Wu"

Article Synopsis
  • Researchers worked on a way to better understand balance by looking at how muscles affect it using computer data.
  • They used special 3D data of the lower legs and a program called OpenSim to calculate forces from muscles.
  • Finally, they created a super-smart computer model (a neural network) that could predict muscle forces really accurately, helping doctors understand balance issues better.
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Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations.

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Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications.

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There is currently a need to culture cells in 3D to better mimic the behaviour of cells growing in the natural environment. In parallel, this calls for novel technologies to assess cell growth in 3D cell culture. In this study, we demonstrated both in silico and in vitro that cell viability inside large cell spheroids could be monitored in real time and label-free with electrical impedance tomography (EIT).

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In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability.

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