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

Reinforcing capability of cellulose nanocrystals obtained from pine cones in a biodegradable poly(3-hydroxybutyrate)/poly(e-caprolactone) (PHB/PCL) thermoplastic blend

[EN] In this work, different loads (3, 5 and 7 wt%) of pine cone cellulose nanocrystals (CNCs) were added to films of poly(3-hydroxybutyrate)/poly(epsilon-caprolactone) (PHB/PCL) blends with a composition of 75 wt% PHB and 25 wt % PCL (PHB75/PCL25). The films were obtained after solvent casting followed by melt compounding in an extruder and finally subjected to a thermocompression process. The influence of different CNCs loadings on the mechanical, thermal, optical, wettability and disintegration in controlled compost properties of the PHB75/PCL25 blend was discussed. Field emission scanning electron microscopy (FESEM) revealed the best dispersion of CNCs on the polymeric matrix was at a load of 3 wt%. Over this loading, CNCs aggregates were formed enhancing the films fragilization due to stress concentration phenomena. However, the addition of CNCs improved the optical properties of the PHB75/PCL25 films by increasing their transparency and accelerated the film disintegration in controlled soil conditions. In general, the blend with 3 wt% CNCs offers the best balanced properties in terms of mechanical, thermal, optical and wettability.This research was supported by the Ministry of Economy and Competitiveness MINECO through the gran number MAT2014-59242-C2-1-R. D. Garcia-Garcia wants to thank the Spanish Ministry of Education, Culture and Sports for the financial support through a FPU grant number FPU13/06011.Garcia-Garcia, D.; Balart, R.; Strömberg, E.; Moriana, R. (2018). Reinforcing capability of cellulose nanocrystals obtained from pine cones in a biodegradable poly(3-hydroxybutyrate)/poly(e-caprolactone) (PHB/PCL) thermoplastic blend. European Polymer Journal. 104:10-18. https://doi.org/10.1016/j.eurpolymj.2018.04.036S101810