AUTOMAC: AUTOmated Mouse behAviour reCognition
Lead Research Organisation:
Queen's University Belfast
Department Name: Sch of Electronics, Elec Eng & Comp Sci
Abstract
Context of the research
Neurodegenerative diseases are characterised by motor deficiencies. For many of them, there are no successful neuroprotective or neuroregenerative therapies clinically available. In order to address this problem, the development of valid animal models for motor disorders has become active growing and vibrant field in preclinical research. Behaviour analysis of laboratory animals has been recognised as a useful tool to assess therapeutic efficacy. The entire process consists of animal tracking and motion categorisation. Despite tremendous efforts made within the research community, there is no system which can perform reliable recognition of complex animal behaviours and interactions. In this project, a fully automated and trainable computer vision system is proposed to monitor and analyse complex mouse behaviours and interactions using video data recorded by calibrated cameras.
Aims and objectives
Scientific:
(1) To develop a system of combining multi-camera tracking and a Hidden Markov Model.
(2) To improve the multi-camera tracking performance combining covariance descriptors and probabilistic data association.
(3) To accelerate K-means clustering using an approximate nearest neighbour algorithm.
Practical:
(1) To improve the performance and scalability of the existing behaviour analysis systems.
(2) To widen the scope of the applicability of the developed tracker and the behaviour recognition system.
(3) To associate healthcare applications with the image and vision computing community.
Potential applications and benefits
(1) This project will help researchers in the healthcare/medical community to significantly reduce annotation time/errors and hence improve medical research quality. The research outcomes of the proposed multidisciplinary project can reach both ICT and healthcare communities by our attendance at conferences in different domains. In the meantime, the research communities will benefit from our publications in journals and publicly accessible tools/databases for sharing skills and experiences.
(2) The proposed research may be commercialised in the form of software tools. The UK hosts many companies that offer services related to the treatment of neurodegenerative diseases, e.g. GSK, and Orion pharmaceutical companies. These companies and their clients stand to profit from improvements in disease modelling and diagnosis/treatment techniques that depend on animal modelling using a system like that proposed in this project. Significant scientific improvements in this field will have a transformative effect on these businesses.
(3) The technologies developed in this project can be directly transferred and applied in physical security, human computer interface and virtual reality. The tools developed in the proposed research can be used to monitor moving objects (e.g. humans and animals) in different set-ups (e.g. authentic and virtual environments). The software package produced in the proposed research can easily find its customers in manufacture and design, basic science, communication engineering, media and entertainment. As a result, there is great potential for wealth creation and boosted economic prosperity from the developed software package for a wider range of applications.
Neurodegenerative diseases are characterised by motor deficiencies. For many of them, there are no successful neuroprotective or neuroregenerative therapies clinically available. In order to address this problem, the development of valid animal models for motor disorders has become active growing and vibrant field in preclinical research. Behaviour analysis of laboratory animals has been recognised as a useful tool to assess therapeutic efficacy. The entire process consists of animal tracking and motion categorisation. Despite tremendous efforts made within the research community, there is no system which can perform reliable recognition of complex animal behaviours and interactions. In this project, a fully automated and trainable computer vision system is proposed to monitor and analyse complex mouse behaviours and interactions using video data recorded by calibrated cameras.
Aims and objectives
Scientific:
(1) To develop a system of combining multi-camera tracking and a Hidden Markov Model.
(2) To improve the multi-camera tracking performance combining covariance descriptors and probabilistic data association.
(3) To accelerate K-means clustering using an approximate nearest neighbour algorithm.
Practical:
(1) To improve the performance and scalability of the existing behaviour analysis systems.
(2) To widen the scope of the applicability of the developed tracker and the behaviour recognition system.
(3) To associate healthcare applications with the image and vision computing community.
Potential applications and benefits
(1) This project will help researchers in the healthcare/medical community to significantly reduce annotation time/errors and hence improve medical research quality. The research outcomes of the proposed multidisciplinary project can reach both ICT and healthcare communities by our attendance at conferences in different domains. In the meantime, the research communities will benefit from our publications in journals and publicly accessible tools/databases for sharing skills and experiences.
(2) The proposed research may be commercialised in the form of software tools. The UK hosts many companies that offer services related to the treatment of neurodegenerative diseases, e.g. GSK, and Orion pharmaceutical companies. These companies and their clients stand to profit from improvements in disease modelling and diagnosis/treatment techniques that depend on animal modelling using a system like that proposed in this project. Significant scientific improvements in this field will have a transformative effect on these businesses.
(3) The technologies developed in this project can be directly transferred and applied in physical security, human computer interface and virtual reality. The tools developed in the proposed research can be used to monitor moving objects (e.g. humans and animals) in different set-ups (e.g. authentic and virtual environments). The software package produced in the proposed research can easily find its customers in manufacture and design, basic science, communication engineering, media and entertainment. As a result, there is great potential for wealth creation and boosted economic prosperity from the developed software package for a wider range of applications.
Planned Impact
Neurodegenerative diseases are characterised by motor deficiencies. For many of them, there are no successful neuroprotective or neuroregenerative therapies clinically available. In order to address this problem, the development of valid animal models for motor disorders has become an active field in preclinical research. Behaviour analysis of laboratory animals has been recognised as a useful tool to assess therapeutic efficacy. The entire process consists of animal tracking and motion categorisation. Despite tremendous efforts made within the research community, there is no system which can perform reliable recognition of complex animal behaviours and interactions. In this project, a fully automated and trainable computer vision system is proposed to monitor and analyse complex mouse behaviours and interactions using video data recorded by calibrated cameras.
Who will benefit from this research?
The direct beneficiaries of this project are organisations in Information, Communications Technologies and healthcare sectors. Research groups in these areas will benefit from our published research results and tools created from this work which will be made available as open source software in a Matlab format. In addition, pharmaceutical companies will benefit from the showcasing of our research results by contributing to the progress of clinical translational research. Furthermore the proposed research holds the potential to benefit industrial applications such as physical security (e.g. IBM and Tyco) and media production (e.g. Northern Ireland Screen and Wolfhound Media for animation).
How will they benefit from this research?
(1) Currently behaviour analysis of laboratory animals for neurodegenerative disease related research is still undertaken manually. This process is extremely tedious and labour-intensive with inter-annotator biases. This project, if successful, will help researchers in the healthcare/medical community to significantly reduce annotation time/errors and hence improve medical research quality. The research outcomes of the proposed multidisciplinary project can reach both ICT and healthcare communities by our attendance at conferences in different domains, e.g. British Machine Vision Conference in 2015 and World Parkinson Congress in 2016. In the meantime, the research communities will benefit from our publications in journals and publicly accessible tools/databases for sharing skills and experiences.
(2) The proposed research may be commercialised in the form of software tools. The UK hosts many companies that offer services related to the treatment of neurodegenerative diseases, e.g. GSK, and Orion pharmaceutical companies. These companies and their clients stand to profit from improvements in disease modelling and diagnosis/treatment techniques that depend on animal modelling using a system like that proposed in this project. Significant scientific improvements in this field will have a transformative effect on these businesses. As diagnosis and treatment of neurodegenerative diseases is a global challenge, UK products and services offered in this area benefit from the proposed research by commercialising and exploiting research results of this project and contributing to the creation international commercial opportunities.
(3) The technologies developed in this project can be directly transferred and applied in physical security, human computer interface and virtual reality. The tools developed in the proposed research can be used to monitor moving objects (e.g. humans and animals) in different set-ups (e.g. authentic and virtual environments). The software package produced in the proposed research can easily find its customers in manufacture and design, basic science, communication engineering, media and entertainment. As a result, wealth creation and economic prosperity will follow from the developed software package for a wider range of applications.
Who will benefit from this research?
The direct beneficiaries of this project are organisations in Information, Communications Technologies and healthcare sectors. Research groups in these areas will benefit from our published research results and tools created from this work which will be made available as open source software in a Matlab format. In addition, pharmaceutical companies will benefit from the showcasing of our research results by contributing to the progress of clinical translational research. Furthermore the proposed research holds the potential to benefit industrial applications such as physical security (e.g. IBM and Tyco) and media production (e.g. Northern Ireland Screen and Wolfhound Media for animation).
How will they benefit from this research?
(1) Currently behaviour analysis of laboratory animals for neurodegenerative disease related research is still undertaken manually. This process is extremely tedious and labour-intensive with inter-annotator biases. This project, if successful, will help researchers in the healthcare/medical community to significantly reduce annotation time/errors and hence improve medical research quality. The research outcomes of the proposed multidisciplinary project can reach both ICT and healthcare communities by our attendance at conferences in different domains, e.g. British Machine Vision Conference in 2015 and World Parkinson Congress in 2016. In the meantime, the research communities will benefit from our publications in journals and publicly accessible tools/databases for sharing skills and experiences.
(2) The proposed research may be commercialised in the form of software tools. The UK hosts many companies that offer services related to the treatment of neurodegenerative diseases, e.g. GSK, and Orion pharmaceutical companies. These companies and their clients stand to profit from improvements in disease modelling and diagnosis/treatment techniques that depend on animal modelling using a system like that proposed in this project. Significant scientific improvements in this field will have a transformative effect on these businesses. As diagnosis and treatment of neurodegenerative diseases is a global challenge, UK products and services offered in this area benefit from the proposed research by commercialising and exploiting research results of this project and contributing to the creation international commercial opportunities.
(3) The technologies developed in this project can be directly transferred and applied in physical security, human computer interface and virtual reality. The tools developed in the proposed research can be used to monitor moving objects (e.g. humans and animals) in different set-ups (e.g. authentic and virtual environments). The software package produced in the proposed research can easily find its customers in manufacture and design, basic science, communication engineering, media and entertainment. As a result, wealth creation and economic prosperity will follow from the developed software package for a wider range of applications.
Organisations
- Queen's University Belfast (Lead Research Organisation)
- Beihang University (Collaboration)
- University of Manchester (Collaboration)
- COVENTRY UNIVERSITY (Collaboration)
- Xidian University (Collaboration)
- University of Sydney (Collaboration)
- Harbin Institute of Technology (Collaboration)
- QUEEN'S UNIVERSITY BELFAST (Collaboration)
- University of Bristol (Collaboration)
Publications
Zhang K
(2019)
Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.
in Computational intelligence and neuroscience
Zhang L
(2018)
BoMW: Bag of Manifold Words for One-Shot Learning Gesture Recognition From Kinect
in IEEE Transactions on Circuits and Systems for Video Technology
Zhang R
(2019)
Fuzzy Optimal Energy Management for Fuel Cell and Supercapacitor Systems Using Neural Network Based Driving Pattern Recognition
in IEEE Transactions on Fuzzy Systems
Zhang S
(2018)
Point-to-Set Distance Metric Learning on Deep Representations for Visual Tracking
in IEEE Transactions on Intelligent Transportation Systems
Zhang S
(2017)
A Biologically Inspired Appearance Model for Robust Visual Tracking.
in IEEE transactions on neural networks and learning systems
Zhang T
(2022)
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images.
in IEEE transactions on cybernetics
Zhang X
(2018)
Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral Imagery
in IEEE Transactions on Geoscience and Remote Sensing
Zhang X
(2018)
Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification
in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Zhang X
(2018)
Multifeature Hyperspectral Image Classification With Local and Nonlocal Spatial Information via Markov Random Field in Semantic Space
in IEEE Transactions on Geoscience and Remote Sensing
Zhang X
(2018)
Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning
in Remote Sensing
| Description | Manual measurement of mouse behaviour is highly labour intensive and prone to error. This investigation aims to efficiently and accurately recognise individual mouse behaviours in continuous videos. In general, the major novel technologies developed in this funded project include: (1) Variants of standard deep neural networks and autoencoder for object detection and recognition. (2) A novel Markov Chain method for motion trajectory clustering. (3) New bag of words based methods for gesture recognition. (4) New transfer learning techniques for object recognition. (5) New evidential reasoning tools for abnormal event detection on public transport vehicles. (6) New deep learning techniques for mouse motion analysis. (7) New low-ranking feature representation methods for image segmentation. |
| Exploitation Route | The proposed research will directly benefit computer vision and movement disorder researchers, who make continuous efforts to reducing the inherent high labour cost and inter-investigator variability associated with the manual annotation of mouse behaviours. The traditional systems strongly rely on the use of specialised sensors and are thus limited to the analysis of relatively simple and pre-programmed behaviours. This project, technically still ongoing, will bring the research of mouse behaviour analysis a step forward in the assessment of therapeutic efficacy of neurodegenerative diseases. In addition, the proposed research may be commercialised in the form of software tools and monitoring devices. These commercialised outcomes will be adopted by medical device manufacturers and therefore the immediate beneficiaries of this project consist of pharmaceutical companies, medical associations and healthcare service agencies which perform translational research based on preclinical animal models. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology |
| URL | https://www2.le.ac.uk/departments/informatics/people/huiyu-zhou |
| Description | Research in mouse behaviour analysis starts making impacts on several areas: (1) The camera kit built in this project is used to capture animals' videos in different medical exercises and projects. (2) Animal motion detection technologies developed in this project are transferable and have helped us to win over £500K research grants across several disciplines. (3) Through public seminars and presentations, the public community increases their awareness to the damage and negative impacts of Parkinson's Disease. (4) The technologies developed in this project start attracting the attention of the public and hence have the potential to influence policy making in some areas. |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare |
| Impact Types | Cultural,Societal,Economic,Policy & public services |
| Description | Post-graduate course development at Queen's University Belfast, Web and Mobile App Development (CSC7054) |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | An early warning foetal motion detection system |
| Amount | £2,000 (GBP) |
| Organisation | Puffin Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 05/2017 |
| End | 04/2018 |
| Description | Centre for Secure Information Technologies |
| Amount | £5,510,546 (GBP) |
| Organisation | Invest Northern Ireland |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2017 |
| End | 01/2022 |
| Description | Pathological cancer image analysis |
| Amount | £298,887 (GBP) |
| Organisation | Invest Northern Ireland |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2016 |
| End | 05/2018 |
| Description | Plasmofluidic analytical device: a rapid point-of-sampling diagnostic and management support platform for infectious and antimicrobial resistant pathogens |
| Amount | £187,470 (GBP) |
| Organisation | Invest Northern Ireland |
| Sector | Public |
| Country | United Kingdom |
| Start | 11/2016 |
| End | 10/2018 |
| Description | Smart distribution grid: a market driven approach for the next generation of advanced operation models and services |
| Amount | € 3,996,125 (EUR) |
| Funding ID | 771066 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 10/2017 |
| End | 03/2021 |
| Description | Smartphone Analyzers for On-site Testing of Food Quality and Safety |
| Amount | £569,130 (GBP) |
| Funding ID | 720325 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 01/2017 |
| End | 12/2020 |
| Description | Using game theory to assess the effects of social norms and social networks on adolescenet smoking in schools: a proof of concept study |
| Amount | £727,000 (GBP) |
| Funding ID | MR/R011176/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 01/2021 |
| Description | Video traffic analysis for abnormal event detection |
| Amount | £105,000 (GBP) |
| Funding ID | NA160342 |
| Organisation | The Royal Society |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 03/2017 |
| End | 02/2020 |
| Title | Mouse behaviour analysis |
| Description | This tool allows one to automate monitoring of mouse movements and interaction in a home cage. The key contribution of the proposed method is based on Hidden Markov Models with deep learning to handle the uncertainty and ambiguity in the estimation of actions in dynamic environments. |
| Type Of Material | Physiological assessment or outcome measure |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Impact | The developed tool has the potential to change the human assessment criteria currently recruited in the medical science. |
| Description | China-UK |
| Organisation | Beihang University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | Coventry University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | Harbin Institute of Technology |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | Queen's University Belfast |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | University of Bristol |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | University of Sydney |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Description | China-UK |
| Organisation | Xidian University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Algorithmic design and evaluation. |
| Collaborator Contribution | Algorithmic design and evaluation. |
| Impact | Zhang, X., Zhang, J., Li, C., Cheng, C., Jiao, L. and Zhou, H. "Hybrid unmixing based on adaptive region segmentation for hyperspectral imagery". IEEE Trans. On Geoscience and Remote Sensing, Accepted. Zhang, L., Zhang, S., Jiang, F., Qi, Y., Zhang, J., Guo, Y. and Zhou, H. "BOMW: Bag of manifold words for one-shot learning gesture recognition from Kinect". IEEE Trans. On Circuits and Systems for Video Technology. Early online publication. Zhang, X., Gao, Z., Jiao, L. and Zhou, H. "Multi-feature hyperspectral image classification with local and non-local spatial information via Markov Random Field in semantic space". IEEE Trans. On Geoscience and Remote Sensing, 56(3), 1409-1424, 2018. Zhang, X., Li, C., Zhang, J., Chen, Q., Feng, J., Jiao, L.C. and Zhou, H. "Hyperspectral unmixing via low-rank representation with space consistency constraint and spectral library pruning". Remote Sensing, 10(2), 339, 2018. Gao, F., Ma, F., Wang, J., Sun, J., Yang, E. and Zhou, H. "Visual saliency modeling for river detection in high-resolution SAR imagery", IEEE Access, Vol. 6, 1000-1014, 2018. Dong, X., Dong, J., Zhou, H., Sun, J. and Tao, D. "Automatic Chinese postal address block location using component-wise proximity descriptors and random forests". IEEE Trans. On Industrial Electronics, 65(5), 4401-4412, 2018. Zhang, S., Qi, Y., Jiang, F., Lan, X., Yuen, P.C. and Zhou, H. "Point-to-set distance metric learning on deep presentations for visual tracking". IEEE Trans. On Intelligent Transportation Systems, 19(1), 187-198, 2018. Li, D., Zhang, S., Zhou, H., Sun, X., Li, S. and Li, X. "Modelling information diffusion over social networks for temporal dynamic prediction". IEEE Trans. On Knowledge and Data Engineering, 29(9), 1985-1997, 2017. Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D. and Li, X. "A biologically inspired appearance model for robust visual tracking". IEEE Trans. On Neural Networks and Learning Systems, 28(10), 2357-2370, 2017. Santofimia, M.J., Martinez del Rincon, J., Hong, X., Zhou, H., Miller, P., Villa, D. and Lopez, J.C. "Hierarchical task network planning with common-sense reasoning for multiple-people behavior analysis". Expert Systems with Applications, 69, 118-134, 2017. Hong, X., Huang, Y., Ma, W., Varadarajan, S., Miller, P., Liu, W., Romero, M.J., del Rincon, J.M. and Zhou, H. "Evidential event inference in transport video surveillance". Computer Vision and Image Understanding, 144, C, 276-297, 2016. Ma, J., Liu, W., Miller, P. and Zhou, H. "An evidential fusion approach for gender profiling". Information Sciences, 333(3), 10-20, 2016. |
| Start Year | 2012 |
| Title | Mouse behaviour recognition |
| Description | Automated recognition of mouse behaviours is crucial in studying psychiatric and neurologic diseases. To achieve this objective, it is very important to analyse temporal dynamics of mouse behaviours. Here, we release the code of our paper for mouse behavior recognition in a video. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | This is the first time that a software package was developed using Hidden Markov Models with deep learning for behaviour recognition. It is innovative in terms of academical contribution and will make a strong impact on animal behaviour analsysis. |
| URL | https://github.com/BIPL-UoL/CMBR |
| Description | British Council Teaching Demo, Nov 2016 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Undergraduate students |
| Results and Impact | Dr. Huiyu Zhou was invited by British Council to give teaching demo to several Chinese universities, including Beijing Institute of Technology, Dalian University of Technology (Panjin), and Harbin University of Engineering/Harbin Institute of Technology. More than 200 under-and post-graduate students and professors from different disciplines attended the lectures that sparked questions and discussions afterwards. These universities reported increased interests in computer vision and animal behaviour analysis. |
| Year(s) Of Engagement Activity | 2016 |
| Description | CECNet Keynote Speech |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | More than 80 people attended this keynote speech on healthcare technology and the audience highly commended the presentation and discussion. The organisers and the conference delegates invited the speaker to give an updated talk on the same conference in 2018. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://www.cecnetconf.org/2017/KeynoteSpeakers.html |
| Description | Chinese Doctoral Forum |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | More than 50 people (academic staff, under- and post-graduates and professionals) attended the forum that provides an academic communication platform for scholars in the UK to share research results, promoting interdisciplinary professional communication, broadening academic horizons, stimulating innovative thinking and improving research ability. Dr. Huiyu Zhou gave a keynote speech of 30 minutes on the topic "Healthcare challenges and machine learning". |
| Year(s) Of Engagement Activity | 2017 |
| Description | Institute visit, 24 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 50 under- and post-graduate students from Xi'an Institute of Optics and Precision Mechanics of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 24 July 2017, Xi'an, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://www.opt.ac.cn/xwzx/tzgg/201707/P020170720407110951835.pdf |
| Description | Invited speech at International Conference on Biomedical Engineering and Biotechnology (ICBEB), Nanjing , China, 17-20 Oct. 2018. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Around 100 students, academic staff, medical doctors and industrial visitors attended the talk which sparked many questions and discussion afterwards. The audience reported increased interest in related subject areas. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at Big Data Centre, Universiti Teknologi Malaysia (UTM), 3-7 December, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | 30+ researchers attended the invited talk, which sparked questions and discussion afterwards, and the university reported increased interest in related subject areas. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at Nanjing University of Science and Technology, China, 18 October, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 30+ postgraduates and academic staff attended the talk, which sparked questions and discussion afterwards, and the audience reported increased interest in related subject areas. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at Queen Mary, University of London, UK, 27 September, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 50+ researchers attended the research talk and had taken part in the question and answer session which is overwhelming. The audience reported increased interest in the medical image processing areas. |
| Year(s) Of Engagement Activity | 2018 |
| URL | http://cis.eecs.qmul.ac.uk/seminars.html |
| Description | Invited talk at Shanghai Jiaotong University, China, 15 October, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 10+ researchers attended the research talk, which sparked questions and discussion afterwards, and the audience reported increased interest in related subject areas. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at University of Exeter, December 2016 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Other audiences |
| Results and Impact | Approximately 30 professors and research students attended the research talk given by Dr. Huiyu Zhou at University of Exeter. This talk, entitled "Mouse Behaviour Recognition", sparked questions and discussion afterwards and the University reported increased interest in image processing and behaviour analysis areas. |
| Year(s) Of Engagement Activity | 2016 |
| Description | Invited talk at University of Kent, UK, 25 April, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 20 postgraduates and academic staff attended this talk and contributed to the discussion during and after the talk. The audience reported increased interest in related subject areas, e.g. machine learning in medical signal processing. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at University of Portsmouth, UK, 19-20 September, 2018. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 20+ postgraduate and academic staff attended the research talk, which sparked many questions and discussion afterwards and the audience reported increased interest in related artificial intelligence and machine learning areas. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Invited talk at University of Ulster, UK, 24 Jan, 2019. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 40+ researchers attended the research talk, which sparked questions and discussion afterwards, and the school reported increased interest in related subject areas. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Medical conference attendance |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | 250 professional practitioners took part in the knowledge sharing and comprehensive discussion on research and management of Parkinson's Disease. The conference attendees actively engaged the questions and answer session and the feedback from the people was very positive. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.parkinsons.org.uk/professionals/events/scotland/parkinsons-disease-200-back-future-confe... |
| Description | Research conference attendance, 15-16 July 2017 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Dr H Zhou attended Medical Image Computing Seminar 2017, held in Shanghai of China, 15-16 July 2017. He communicated his recent research progress on animal mouse behaviour analysis for Parkinson's disease with the conference attendees, which sparked overwhelming questions and discussion. More than 10 different research groups from China and other countries raised major interest to this research study. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://mic.sjtu.edu.cn/mics-2017/home |
| Description | School visit (Bristol) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Approx. 10 researchers and students attended the seminar and discussed the progress in research. |
| Year(s) Of Engagement Activity | 2018 |
| Description | University visit, 17 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 30 under- and post-graduate students from Shanghai University of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 17 July 2017, Shanghai, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 19 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 50 under- and post-graduate students from Ocean University of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 19 July 2017, Qingdao, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 22 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 35 under- and post-graduate students from China University of Petroleum attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 22 July 2017, Qingdao, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 24 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 50 under- and post-graduate students from Xi'an Jiaotong University of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 24 July 2017, Xi'an, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 25 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 50 under- and post-graduate students from Northwest Polytechnic University of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 25 July 2017, Xi'an, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 6 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 40 under- and post-graduate students from Jiangsu University of Science and Technology (JUST) of China attended Dr H Zhou's research talk, entitled "Healthcare challenges and machine learning", 6 July 2017, Zhengjiang, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visit, 7 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 20 under- and post-graduate students from Southeast University of China attended Dr H Zhou's research talk, entitled "Healthcare challenges and machine learning", 7 July 2017, Nanjing, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |
| Description | University visits, 14 July 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | 60 under- and post-graduate students from Nanjing University of Aeronautics and Astronautics (NUAA) of China attended Dr H Zhou's research talk, entitled "Machine Learning for Healthcare Applications", 14 July 2017, Nanjing, China. This talk raised the awareness of the students in animal behaviour analysis and Parkinson's disease, and the students reported increased interest in these subject areas. |
| Year(s) Of Engagement Activity | 2017 |