Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment
Lead Research Organisation:
King's College London
Department Name: Imaging & Biomedical Engineering
Abstract
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People |
ORCID iD |
Tomoki Arichi (Principal Investigator) |
Publications
Schmidtke L
(2021)
Unsupervised Human Pose Estimation through Transforming Shape Templates
Lukens AM
(2021)
Predictive validity of the Lacey Assessment of Preterm Infants for motor outcome at 2 years corrected age.
in Early human development
Dall'Orso S
(2022)
Development of functional organization within the sensorimotor network across the perinatal period.
in Human brain mapping
Description | The award has developed a new method which can estimate motor behaviour and pose in human infants completely non-invasively (using video). This is important as it enables automated analysis which is amenable to exploration with machine learning and can greatly reduce the workload associated with a human observer. Using MRI, we have also studied patterns of functional connectivity in the sensorimotor cortex across the neonatal period. We found that across the preterm period, functional connections within this network are rapidly developing and becoming more specific - highlighting the key importance for this time for this emerging system. |
Exploitation Route | The developed software has been made available for download in a Github repository |
Sectors | Healthcare |
URL | http://www.perinatal-functional-imaging.co.uk/research/ |
Description | Developing responsible neurotechnology for infants and children with neurodevelopmental conditions |
Amount | £1,048,588 (GBP) |
Funding ID | EP/W035154/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 04/2022 |
End | 05/2025 |
Description | Senior Clinical Fellowship: Exploration of cortical structure and function in human infancy with advanced MRI methods |
Amount | £2,138,568 (GBP) |
Funding ID | MR/Y009665/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2024 |
End | 03/2029 |
Description | Collaboration for general movements in infants |
Organisation | Norwegian University of Science and Technology (NTNU) |
Country | Norway |
Sector | Academic/University |
PI Contribution | Research collaboration to use automated methods (using computer vision and deep learning) for studying and classifying infant general movements. |
Collaborator Contribution | Partners are providing large amount of data and access to their methods for comparision with those developed as part of our EPSRC project. |
Impact | none as yet |
Start Year | 2021 |
Description | Collaboration with Chalmers University Sweden |
Organisation | Chalmers University of Technology |
Country | Sweden |
Sector | Academic/University |
PI Contribution | We are collecting 3D camera and electro-magnetic tracking data which is being used to measure infant movements. We are collecting the data. |
Collaborator Contribution | Collaboration with Silvia Muceli who is an expert in human motor control and signal processing. She has post-doctoral fellow and a Marie-Curie fellowship to work on similar work so has been providing expertise about the associated analysis |
Impact | This is a multi-discplinary collaboration with a bioengineering group. This work has only started recently so there are no specific outputs yet. |
Start Year | 2019 |
Title | Software for 'Unsupervised Human Pose Estimation through Transforming Shape Templates" |
Description | Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to generalize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | Software was presented at the prestigious CVPR conference 2021 and has initiated a collboration with researchers at the Norwegian Technical University. |
URL | https://github.com/lschmidtke/shape_templates |
Description | Paper accepted for CVPR (Computer Vision and Pattern Recognition) 2021 conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | "Unsupervised Human Pose Estimation through Transforming Shape Templates; Luca Schmidtke, Simon Ellershaw, Athanasios Vlontzos, Bernhard Kainz, Anna Lukens, Tomoki Arichi" accepted for the prestigious 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The paper presents a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. This will be presented later in the year at the meeting. This meeting reaches a wide audience including academic, industrial, and media outlets as it is the most important meeting in the field annually. |
Year(s) Of Engagement Activity | 2021 |
URL | http://cvpr2021.thecvf.com/ |
Description | Parent Power family engagement event |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | I took part in a "Parent Power" event for families from Lambeth and Southwark organised by King's College London. This event was designed to provide a forum for families from the local community to hear about research and to be able to engage with researchers to ask questions. I spoke about my own work using MRI to study brain development and autism, and then had an open discussion and question/answer session with these families. |
Year(s) Of Engagement Activity | 2021 |
Description | Perinatal Functional Imaging Twitter |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Twitter page (Kings_PFIG) to transmit current views, advertise positions, describe and disseminate current work. Currently 352 followers |
Year(s) Of Engagement Activity | 2018,2019,2020,2021 |
URL | https://twitter.com/Kings_PFIG |
Description | Perinatal Functional Imaging group website |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Academic Group website, developed to provide information about the research work and provide access for resources such as data, analysis tools, and links to publications. The website is updated regularly to reflect current work and provide contract details. |
Year(s) Of Engagement Activity | 2018,2019,2020,2021 |
URL | http://www.perinatal-functional-imaging.co.uk |
Description | William Little Foundation - evaluation of need for early intervention and recognition in cerebral palsy |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Supporters |
Results and Impact | Inivited to participate in the working group and to review the draft document of an assessment and statement from the Willam Little Foundation about early recognition and intervention in cerebral palsy. |
Year(s) Of Engagement Activity | 2021 |