Action recognition using the Rf Transform on optical flow images

The objective of this paper is the automatic recognition of human actions in video sequences. The use of spatio-temporal features for action recognition has become very popular in recent literature Instead of extracting the spatio-temporal features from the raw video sequence, some authors propose to project the sequence to a single template first. As a contribution we propose the use of several variants of the R transform for projecting the image sequences to templates. The R transform projects the whole sequence to a single image, retaining information concerning movement direction and magnitude. Spatio-temporal features are extracted from the template, they are combined using a bag of words paradigm, and finally fed to a SVM for action classification. The method presented is shown to improve the state-of-art results on the standard Weizmann action datasetPeer ReviewedPostprint (published version

Temporal segmentation of human actions in video sequences

Most of the published works concerning action recognition, usually assume that the action sequences have been previously segmented in time, that is, the action to be recognized starts with the first sequence frame and ends with the last one. However, temporal segmentation of actions in sequences is not an easy task, and is always prone to errors. In this paper, we present a new technique to automatically extract human actions from a video sequence. Our approach presents several contributions. First of all, we use a projection template scheme and find spatio-temporal features and descriptors within the projected surface, rather than extracting them in the whole sequence. For projecting the sequence we use a variant of the R transform, which has never been used before for temporal action segmentation. Instead of projecting the original video sequence, we project its optical flow components, preserving important information about action motion. We test our method on a publicly available action dataset, and the results show that it performs very well segmenting human actions compared with the state-of-the-art methods.Peer ReviewedPostprint (author's final draft

Sedation in pediatric palliative care: The role of pediatric palliative care teams

Objectives. Palliative sedation (PS) consists of the use of drugs to alleviate the suffering of patients with refractory symptoms, through a reduction in consciousness.The aim of this study is to describe the incidence of and indications for PS in patients treated by pediatric palliative care teams (PPCT), and the relationship between PS, the place of death, and the characteristics of the care teams. Methods. Ambispective study with the participation of 14 PPCT working in Spain. Results. From January to December 2019, a total of 164 patients attended by these PPCT died. Of these, 83 (50.6%) received PS during their last 24 hours. The most frequent refractory symp toms were terminal suffering (n = 40, 48.2%), dyspnea (n = 9, 10.8%), pain (n = 8, 9.6%), and convulsive state (n = 7, 8.4%). Sedation in the last 24 hours of life was more likely if the patient died in hospital, rather than at home (62.9% vs. 33.3%, p < 0.01); if the parents had not expressed their preference regarding the place of death (69.2% vs. 45.2%, p = 0.009); and if the PPCT had less than 5 years’ experience (66.7% vs. 45.5%, p = 0.018). Significance of results. PS is a real possibility in pediatric end-of-life care and relates to care planning and team expertise.Funding for open access charge: Universidad de Málaga/CBU

Human action recognition by means of subtensor projections and dense trajectories

In last years, most human action recognition works have used dense trajectories features, to achieve state-of-the-art results. Histograms of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms (MBH) features are extracted from regions and being tracked across the frames. The goal of this paper is to improve the performance obtained by means of Improved Dense Trajectories (IDTs), adding new features based on temporal templates. We construct these templates considering a video sequence as a third-order tensor and computing three different projections. We use several functions for projecting the fibers from the video sequences, and combined them by means of sum pooling. As a first contribution of our work, we present in detail the method based on tensor projections. First, we have assessed the results obtained using only template based action recognition. Next, in order to achieve state-of-art recognition rates, we have fused our features with those of IDTs.This is the second contribution of the article. Experiments on four different public datasets have shown that this technique improves IDTs performance and that the results outperform the ones obtained by most of the state-of-the-art techniques for action recognition.Peer Reviewe

Action recognition using the Rf Transform on optical flow images

The objective of this paper is the automatic recognition of human actions in video sequences. The use of spatio-temporal features for action recognition has become very popular in recent literature Instead of extracting the spatio-temporal features from the raw video sequence, some authors propose to project the sequence to a single template first. As a contribution we propose the use of several variants of the R transform for projecting the image sequences to templates. The R transform projects the whole sequence to a single image, retaining information concerning movement direction and magnitude. Spatio-temporal features are extracted from the template, they are combined using a bag of words paradigm, and finally fed to a SVM for action classification. The method presented is shown to improve the state-of-art results on the standard Weizmann action datasetPeer Reviewe

Human action recognition by means of subtensor projections and dense trajectories

In last years, most human action recognition works have used dense trajectories features, to achieve state-of-the-art results. Histograms of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms (MBH) features are extracted from regions and being tracked across the frames. The goal of this paper is to improve the performance obtained by means of Improved Dense Trajectories (IDTs), adding new features based on temporal templates. We construct these templates considering a video sequence as a third-order tensor and computing three different projections. We use several functions for projecting the fibers from the video sequences, and combined them by means of sum pooling. As a first contribution of our work, we present in detail the method based on tensor projections. First, we have assessed the results obtained using only template based action recognition. Next, in order to achieve state-of-art recognition rates, we have fused our features with those of IDTs.This is the second contribution of the article. Experiments on four different public datasets have shown that this technique improves IDTs performance and that the results outperform the ones obtained by most of the state-of-the-art techniques for action recognition.Peer Reviewe

Temporal segmentation of human actions in video sequences

Most of the published works concerning action recognition, usually assume that the action sequences have been previously segmented in time, that is, the action to be recognized starts with the first sequence frame and ends with the last one. However, temporal segmentation of actions in sequences is not an easy task, and is always prone to errors. In this paper, we present a new technique to automatically extract human actions from a video sequence. Our approach presents several contributions. First of all, we use a projection template scheme and find spatio-temporal features and descriptors within the projected surface, rather than extracting them in the whole sequence. For projecting the sequence we use a variant of the R transform, which has never been used before for temporal action segmentation. Instead of projecting the original video sequence, we project its optical flow components, preserving important information about action motion. We test our method on a publicly available action dataset, and the results show that it performs very well segmenting human actions compared with the state-of-the-art methods.Peer Reviewe

Fast synthesis of micro/mesoporous xerogels: Textural and energetic assessment

The sol–gel polymerization of resorcinol/formaldehyde mixtures to obtain porous gels is typically a long process performed throughout several days. In this work, we have explored an experimental approach to reduce the time necessary to obtain porous gels based on mild polymerization conditions and direct drying. We have analyzed the effects of the temperature and time of the gelation/aging step on the porosity of the gels, as well as the impact on the overall energetic cost of the process. Data have shown that well-developed micro–mesoporous architectures can be obtained within less than a day. The temperature of the gelation/aging step mainly affects the mesopore network, whereas the microporosity is determined by the composition of the precursor’s mixture. The exclusion of the solvent exchange step yields soft mechanically fragile porous gels with structural limitations upon carbonization at high temperature in inert atmosphere, due to the surface tensions applied to the backbone during the evolution of volatiles. The mesopore structure lost during carbonization is not recovered upon activation in CO2 atmosphere, but it is preserved upon chemical activation in K2CO3 and the resulting gel exhibits a bimodal micro–mesoporous distribution. Furthermore, the energy savings of this route are similar to those obtained using microwave-heating in terms of grams of xerogel per kilowatt hour of energy consumed for similar textural properties. The correlation between the energy power consumed and the textural parameters is a useful tool to optimize the synthesis.The authors thank the financial support of the Spanish MINECO (grant CTM2011/023378) and Fondos Feder PCTI Asturias (grant PC10-002). EDIP and RJC are grateful to CONACyT and PCTI Asturias for the mobility grant (290674) and Severo Ochoa fellowship, respectively. COA acknowledges Dr. Jacek Jagiello for the fruitful discussion on 2D-NLDFT-HS model.Peer reviewe

Protocol for a multi-phase, multi-center, real-world, hybrid effectiveness–implementation study of a digital intervention for pediatric chronic pain co-designed with patients (Digital SPA)

Background Children and adolescents with chronic pain are a vulnerable population who often lack the resources to manage their condition. Due to high personal, social, and economic consequences, proper management in its early stages is key to reducing disability. The aim of this project is to co-develop a digital intervention for pediatric chronic pain (Digital SPA) with end-users and to evaluate its effectiveness and implementation outcomes in Spain. Methods (Phase 1) Focus groups with patients, parents, and clinicians ( n  = 5–6 each) will inform about unmet pain care needs and provide a starting point for co-designing the intervention. (Phase 2) Content creation and usability testing will be based on the results of Phase 1, and the theory-driven development will follow the latest available evidence. The intervention will use validated psychological techniques focused on improving functioning by teaching pain coping skills. (Phase 3) Hybrid effectiveness–implementation trial. Participants ( n  = 195) will be adolescents aged 12–17 years old with chronic pain and one of their parents. Assessments include physical function, pain, sleep, anxiety, mood, satisfaction and adherence to the treatment, and number of visits to the emergency room. A qualitative framework analysis will be conducted with data from Phase 1. Effects of the intervention will be evaluated using linear multilevel modeling. The Consolidated Framework for Implementation Research (CFIR) and Behavioral Interventions Using Technology (BIT) frameworks will be used to evaluate implementation. Discussion This study is expected to produce a co-created evidence-based digital intervention for pediatric chronic pain and a roadmap for successful implementation. Trial registration number (TRN) and date of registration ClinicalTrials.gov (registered on 26 June 2023: https://clinicaltrials.gov/study/NCT05917626 ). Contributions to the literature The implementation of digital health interventions has two major gaps: (1) adherence to treatment is suboptimal, and (2) the process of making the interventions available to the end-user in a sustainable way is often unsuccessful. In this study, we expect that assessing users’ needs and co-designing an intervention with them will improve adherence. Documenting the implementation process from the project inception and integrating the results into an implementation framework will allow for replication and extension in different contexts. This study will increase the knowledge about implementation in a vulnerable population: adolescents with chronic pain without access to in-person multidisciplinary pain care