Pain rehabilitation: E/Motion-based automated coaching
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Maja Pantic (Principal Investigator) |
Publications
Trigeorgis G
(2016)
Deep Canonical Time Warping
Trigeorgis G
(2017)
A Deep Matrix Factorization Method for Learning Attribute Representations.
in IEEE transactions on pattern analysis and machine intelligence
Tzimiropoulos G
(2014)
Active Orientation Models for Face Alignment In-the-Wild
in IEEE Transactions on Information Forensics and Security
Valstar M
(2011)
The first facial expression recognition and analysis challenge
Valstar M
(2013)
AVEC 2013
Valstar MF
(2012)
Fully automatic recognition of the temporal phases of facial actions.
in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Valstar MF
(2012)
Meta-Analysis of the First Facial Expression Recognition Challenge.
in IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Wang M
(2018)
Disentangling the Modes of Variation in Unlabelled Data.
in IEEE transactions on pattern analysis and machine intelligence
Wang Y
(2018)
Face Mask Extraction in Video Sequence
in International Journal of Computer Vision
Zafeiriou L
(2017)
Nonnegative Decompositions for Dynamic Visual Data Analysis.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
| Description | (a) Pain intensity, as shown in rehabilitation-related scenarios, can be automatically estimated from facial expressions with high Pearson correlation coefficient (CORR >= 0.5). This can be done either by firstly recognising facial actions (i.e. facial action units) underlying the expression of pain, or by estimating the intensity of facial expression of pain directly from the extent of changes in facial features such as the displacement of facial characteristic points. (b) The best results are achieved if accurate facial point trackers are used and facial point locations and displacements are used to represent changes in the observed facial expressions. (c) Discriminative machine learning approaches perform robustly for the target problem (i.e. pain intensity estimation) but cannot handle missing data, which is typical in real-world scenarios as occlusions and self-occlussions often occur. For this problem, it has been shown that a generative approach (i.e. newly-proposed Latent Trees) has a superior performance. |
| Exploitation Route | Some of the developed methodologies are publicly available in http://ibug.doc.ic.ac.uk/resources |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare |
| URL | http://www.uclic.ucl.ac.uk/people/n.berthouze/EPain.html |
| Description | The consortium collected a large database of multimodal recordings of human behaviour in rehabilitation scenario in which they experienced pain while performing rehabilitation exercises. The database has been properly documented, annotated in terms of pain level as judged by human experts, and released according to ethical clearance guidelines. This database has a very large potential impact as it allows academics and scientists all over the world to study the problem of pain estimation by humans and machines based on various signals including facial expressions captured at a very high frequency and resolution. |
| First Year Of Impact | 2015 |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare |
| Impact Types | Societal |