MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain

Wearable cameras allow to acquire images and videos from the user's perspective. These data can be processed to understand humans behavior. Despite human behavior analysis has been thoroughly investigated in third person vision, it is still understudied in egocentric settings and in particular in industrial scenarios. To encourage research in this field, we present MECCANO, a multimodal dataset of egocentric videos to study humans behavior understanding in industrial-like settings. The multimodality is characterized by the presence of gaze signals, depth maps and RGB videos acquired simultaneously with a custom headset. The dataset has been explicitly labeled for fundamental tasks in the context of human behavior understanding from a first person view, such as recognizing and anticipating human-object interactions. With the MECCANO dataset, we explored five different tasks including 1) Action Recognition, 2) Active Objects Detection and Recognition, 3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and 5) Next-Active Objects Detection. We propose a benchmark aimed to study human behavior in the considered industrial-like scenario which demonstrates that the investigated tasks and the considered scenario are challenging for state-of-the-art algorithms. To support research in this field, we publicy release the dataset at https://iplab.dmi.unict.it/MECCANO/.Comment: arXiv admin note: text overlap with arXiv:2010.0565

StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

Anticipation problem has been studied considering different aspects such as predicting humans' locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022. Please see the project web page for code and additional details: https://iplab.dmi.unict.it/stillfast/

Occurrence of Centrouropoda almerodai and Uroobovella marginata (Acari : Uropodina) phoretic on the Red Palm Weevil in Malta

The unwanted introduction of the Red Palm Weevil (RPW) coincides with the spread in Malta of two species of Uropodid mites associated with this weevil. Usually, adult RPW carry phoretic forms of C. almerodai which are attached to the underside of elytrae, and U. marginata that prefers exposed surfaces of sternum, pygidium, head and legs. These mites use adult RPW to abandon dead palms and to colonize newly infested host-plants. Their role as plant pests is however negligible. Even the plant pathogen conidia, Curvularia which are carried by the mites, seem unable to germinate in palms under laboratory conditions. Both Centrouropoda almerodai and Uroobovella marginata are established in the Maltese Islands.peer-reviewe

General methods for measuring and comparing medical interventions in childbirth: a framework

Abstract Background: The continue increase of interventions during labour in low risk population is a controversial issue of the current obstetric literature, given the lack of evidence demonstrating the benefits of unnecessary interventions for women or infants’ health. This makes it important to have approaches to assess the burden of all medical interventions performed. Methods: Exploiting the nature of childbirth intervention as a staged process, we proposed graphic representations allowing to generate alternative formulas for the simplest measures of the intervention intensity namely, the overall and type-specific treatment ratios. We applied the approach to quantify the change in interventions following a protocol termed Comprehensive Management (CM), using data from Robson classification, collected in a prospective longitudinal cohort study carried out at the Obstetric Unit of the Cà Granda Niguarda Hospital in Milan, Italy. Results: Following CM a substantial reduction was observed in the Overall Treatment Ratio, as well as in the ratios for augmentation (amniotomy and synthetic oxytocin use) and for caesarean section ratio, without any increase in neonatal and maternal adverse outcomes. The key component of this reduction was the dramatic decline in the proportion of women progressing to augmentation, which resulted not only the most practiced intervention, but also the main door towards further treatments. Conclusions: The proposed framework, once combined with Robson Classification, provides useful tools to make medical interventions performed during childbirth quantitatively measurable and comparable. The framework allowed to identifying the key components of interventions reduction following CM. In its turn, CM proved useful to reduce the number of medical interventions carried out during childbirth, without worsening neonatal and maternal outcomes

Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario

In this paper, we tackle the problem of Egocentric Human-Object Interaction (EHOI) detection in an industrial setting. To overcome the lack of public datasets in this context, we propose a pipeline and a tool for generating synthetic images of EHOIs paired with several annotations and data signals (e.g., depth maps or segmentation masks). Using the proposed pipeline, we present EgoISM-HOI a new multimodal dataset composed of synthetic EHOI images in an industrial environment with rich annotations of hands and objects. To demonstrate the utility and effectiveness of synthetic EHOI data produced by the proposed tool, we designed a new method that predicts and combines different multimodal signals to detect EHOIs in RGB images. Our study shows that exploiting synthetic data to pre-train the proposed method significantly improves performance when tested on real-world data. Moreover, to fully understand the usefulness of our method, we conducted an in-depth analysis in which we compared and highlighted the superiority of the proposed approach over different state-of-the-art class-agnostic methods. To support research in this field, we publicly release the datasets, source code, and pre-trained models at https://iplab.dmi.unict.it/egoism-hoi

Sarcopenic obesity and health outcomes: An umbrella review of systematic reviews with meta‐analysis

Many studies support the idea that sarcopenic obesity (SO) could be considered a potential risk factor for negative health outcomes. These results have been inconsistent, and no umbrella reviews exist regarding this topic. Several databases until November 2023 were searched for systematic reviews with meta-analysis of observational studies (cross-sectional, case–control and prospective). For each association, random-effects summary effect sizes with correspondent 95% confidence intervals (CIs) were evaluated using the GRADE tool. Among the 213 papers initially screened, nine systematic reviews with meta-analysis were included, for a total of 384 710 participants. In cross-sectional and case–control studies, 30 different outcomes were analysed, and 18 were statistically significant. In any population addressed in cross-sectional and case–control studies, compared with non-SO, SO increased the prevalence of cognitive impairment (k = 3; odds ratio [OR] = 3.46; 95% CI: 2.24–5.32; high certainty of evidence), coronary artery disease (k = 2; OR = 2.48; 95% CI: 1.85–3.31) and dyslipidaemia (k = 3; OR = 2.50; 95% CI: 1.51–4.15). When compared with sarcopenia or obesity, the results were conflicting. In prospective studies, the association between SO—compared with non-SO—and other negative outcomes was supported by low/very low certainty of evidence and limited to a few conditions. Besides, no comparison with sarcopenia or obesity was provided. Finally, only a few studies have considered muscle function/physical performance in the diagnostic workup. SO could be considered a risk factor only for a few conditions, with the literature mainly based on cross-sectional and case–control studies. Future studies with clear definitions of SO are needed for quantifying the importance of SO—particularly when compared with the presence of only sarcopenia or obesity—and the weight of muscle function/physical performance in its definition

ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios

ENIGMA-51 is a new egocentric dataset acquired in a real industrial domain by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and electronic instruments (e.g., oscilloscope). The 51 sequences are densely annotated with a rich set of labels that enable the systematic study of human-object interactions in the industrial domain. We provide benchmarks on four tasks related to human-object interactions: 1) untrimmed action detection, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human-object interactions in industrial scenarios. We publicly release the dataset at: https://iplab.dmi.unict.it/ENIGMA-51/

Efficacy and safety of niacin/laropiprant therapy in familial hypercholesterolemic patients with coronary artery disease

Background: Cardiovascular disease is the principal cause of premature mortality and morbidity in Europe. Patients with familial hypercholesterolemia are at particularly increased risk and, despite lipid-lowering therapy, continue to experience cardiovascular events. Currently, for these patients a new treatment option is represented by extended-release niacin/laropiprant (ERN/LRPN). Material and Methods: We followed-up for 16 weeks a group of 23 familial hypercholesterolemic patients (mean age 61?7 years, 74% male) with chronic coronary artery disease and ERN/LRPN added on top of maximally tolerated lipid-lowering therapy. ERN/LRPN was administered at the dose of 1 gr/day for the first 4 weeks and then at 2 gr/day for the remaining period. Clinical examination and blood sampling (including lipid profile, renal and hepatic function) were performed at baseline, after 4 weeks, at the end of follow-up, and in the case of eventual clinical manifestations. Results: During follow-up, 14 patients discontinued therapy due to side effects (headache, asthenia, and gastrointestinal disorders in 4 patients, muscle aches and CK increase in 3 patients, eruptive skin rash in 2 patients, onset of diabetes mellitus in 2 patients, dizziness associated with inability to drive in 1 patient, acute hepatitis in 1 patient and palpitations in 1 patient) and 2 patients voluntarily interrupted the therapy. In the remaining 7 patients, an improvement in lipid profile was observed (total cholesterol -14%, HDL cholesterol +7%, LDL cholesterol -16%, Triglycerides -53%, Apolipoprotein A1 +8%, Apolipoprotein B -21%, Apolipoprotein E -31%) in the absence of substantial changes in other laboratory analyses (with the exception of a non-significant increase in uric acid). Intolerable skin flushing was not observed in any patient. In addition, among patients who did report flushing, a reduction in the incidence of the episodes was observed after the first month of therapy

An Outlook into the Future of Egocentric Vision

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.Comment: We invite comments, suggestions and corrections here: https://openreview.net/forum?id=V3974SUk1