Pain rehabilitation: E/Motion-based automated coaching
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
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Organisations
People |
ORCID iD |
| Maja Pantic (Principal Investigator) |
Publications
Alabort-I-Medina J
(2017)
A Unified Framework for Compositional Fitting of Active Appearance Models.
in International journal of computer vision
Alabort-I-Medina J
(2015)
Unifying holistic and Parts-Based Deformable Model fitting
Alabort-I-Medina J
(2014)
Menpo
Alabort-I-Medina J
(2014)
Bayesian Active Appearance Models
Antonakos E
(2015)
Feature-based Lucas-Kanade and active appearance models.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Antonakos E
(2014)
HOG active appearance models
Antonakos E
(2015)
A survey on mouth modeling and analysis for Sign Language recognition
Antonakos E
(2015)
Active Pictorial Structures
Antonakos E
(2014)
Automatic Construction of Deformable Models In-the-Wild
Asthana A
(2015)
From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild.
in IEEE transactions on pattern analysis and machine intelligence
Asthana A
(2014)
Incremental Face Alignment in the Wild
Aung MSH
(2016)
The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset.
in IEEE transactions on affective computing
Bilakhia S
(2013)
Audiovisual Detection of Behavioural Mimicry
Bilakhia S
(2015)
The MAHNOB Mimicry Database: A database of naturalistic human interactions
in Pattern Recognition Letters
Booth J
(2014)
Optimal UV spaces for facial morphable model construction
Booth J
(2018)
Large Scale 3D Morphable Models.
in International journal of computer vision
Booth J
(2016)
A 3D Morphable Model Learnt from 10,000 Faces
Booth J
(2017)
3D Face Morphable Models "In-the-Wild"
Bousmalis K
(2015)
Variational Infinite Hidden Conditional Random Fields
in IEEE Transactions on Pattern Analysis and Machine Intelligence
Cheng S
(2014)
Real-time generic face tracking in the wild with CUDA
Cheng S
(2017)
Statistical non-rigid ICP algorithm and its application to 3D face alignment
in Image and Vision Computing
Cheng S
(2014)
3D facial geometric features for constrained local model
Chrysos G
(2017)
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
in International Journal of Computer Vision
Chrysos G
(2015)
Offline Deformable Face Tracking in Arbitrary Videos
Chugh A
(2023)
Comparative docking studies of drugs and phytocompounds for emerging variants of SARS-CoV-2.
in 3 Biotech
Fotiadou E
(2017)
Temporal Archetypal Analysis for Action Segmentation
Georgakis C
(2014)
Visual-only discrimination between native and non-native speech
Georgakis C
(2014)
Visual-only Discrimination between Native and Non-Native Speech
Georgakis C
(2018)
Dynamic Behavior Analysis via Structured Rank Minimization.
in International journal of computer vision
Georgakis C
(2014)
Discriminating Native from Non-Native Speech Using Fusion of Visual Cues
Georgakis C
(2016)
Discriminant Incoherent Component Analysis
in IEEE Transactions on Image Processing
Jiang B
(2014)
A dynamic appearance descriptor approach to facial actions temporal modeling.
in IEEE transactions on cybernetics
Kaltwang S
(2012)
Advances in Visual Computing
Kaltwang S
(2015)
Latent trees for estimating intensity of Facial Action Units
Liwicki S
(2015)
Online kernel slow feature analysis for temporal video segmentation and tracking.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Liwicki S
(2014)
Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
Marras I
(2014)
Online learning and fusion of orientation appearance models for robust rigid object tracking
in Image and Vision Computing
Martinez B
(2013)
Local evidence aggregation for regression-based facial point detection.
in IEEE transactions on pattern analysis and machine intelligence
Martinez B
(2015)
Facial landmarking for in-the-wild images with local inference based on global appearance
in Image and Vision Computing
Nikitidis S
(2014)
Merging SVMs with Linear Discriminant Analysis: A Combined Model
| 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 |