A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


BibTex format

author = {Zhang, D and Wu, Z and Chen, J and Gao, A and Chen, X and Li, P and Wang, Z and Yang, G and Lo, B and Yang, G-Z},
doi = {10.1109/LRA.2020.2989075},
journal = {IEEE Robotics and Automation Letters},
pages = {4148--4155},
title = {Automatic microsurgical skill assessment based on cross-domain transfer learning},
url = {http://dx.doi.org/10.1109/LRA.2020.2989075},
volume = {5},
year = {2020}

RIS format (EndNote, RefMan)

AB - The assessment of microsurgical skills for Robot-Assisted Microsurgery (RAMS) still relies primarily on subjective observations and expert opinions. A general and automated evaluation method is desirable. Deep neural networks can be used for skill assessment through raw kinematic data, which has the advantages of being objective and efficient. However, one of the major issues of deep learning for the analysis of surgical skills is that it requires a large database to train the desired model, and the training process can be time-consuming. This letter presents a transfer learning scheme for training a model with limited RAMS datasets for microsurgical skill assessment. An in-house Microsurgical Robot Research Platform Database (MRRPD) is built with data collected from a microsurgical robot research platform (MRRP). It is used to verify the proposed cross-domain transfer learning for RAMS skill level assessment. The model is fine-tuned after training with the data obtained from the MRRP. Moreover, microsurgical tool tracking is developed to provide visual feedback while task-specific metrics and the other general evaluation metrics are provided to the operator as a reference. The method proposed has shown to offer the potential to guide the operator to achieve a higher level of skills for microsurgical operation.
AU - Zhang,D
AU - Wu,Z
AU - Chen,J
AU - Gao,A
AU - Chen,X
AU - Li,P
AU - Wang,Z
AU - Yang,G
AU - Lo,B
AU - Yang,G-Z
DO - 10.1109/LRA.2020.2989075
EP - 4155
PY - 2020///
SN - 2377-3766
SP - 4148
TI - Automatic microsurgical skill assessment based on cross-domain transfer learning
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2020.2989075
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000538149900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/9072568
UR - http://hdl.handle.net/10044/1/88073
VL - 5
ER -