1,763 research outputs found
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In this paper, we propose a novel two-stage learning approach for
VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs)
to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels
from face images. The estimated scores are then fed into the personalized
Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by
each person. Personalization of the model is performed using a newly introduced
facial expressiveness score, unique for each person. To the best of our
knowledge, this is the first approach to automatically estimate VAS from face
images. We show the benefits of the proposed personalized over traditional
non-personalized approach on a benchmark dataset for pain analysis from face
images.Comment: Computer Vision and Pattern Recognition Conference, The 1st
International Workshop on Deep Affective Learning and Context Modelin
Pain Level Detection From Facial Image Captured by Smartphone
Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high
Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics
We propose an automatic method for pain intensity measurement from video. For
each video, pain intensity was measured using the dynamics of facial movement
using 66 facial points. Gram matrices formulation was used for facial points
trajectory representations on the Riemannian manifold of symmetric positive
semi-definite matrices of fixed rank. Curve fitting and temporal alignment were
then used to smooth the extracted trajectories. A Support Vector Regression
model was then trained to encode the extracted trajectories into ten pain
intensity levels consistent with the Visual Analogue Scale for pain intensity
measurement. The proposed approach was evaluated using the UNBC McMaster
Shoulder Pain Archive and was compared to the state-of-the-art on the same
data. Using both 5-fold cross-validation and leave-one-subject-out
cross-validation, our results are competitive with respect to state-of-the-art
methods.Comment: accepted at ICPR 2020 Conferenc
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