Rebeldía objetual en tiempos de des-normalización de la funcionalidad

A theoretical journey and thoughts on art and design, as well as a dialogue between works by contemporary Argentine artists that are linked through a common syntax between them, a syntax based on operations and denial strategies of the original functionality of the objects, functionalities that operate from a model that was instilled in us, where objects must serve us, obey and be beautiful. These are objects that struggle with their role model, their canonical or normative condition, they are at the same time real and represented, objects and things, static and dynamic. They are objects that offer us a look at the transformations experienced within the material culture of everyday life. They help us recognize certain concrete aspects that distinguish people's lives and turn particular and social situations into plausible experiences to be narrated.Un recorrido teórico y una reflexión sobre el arte y el diseño, así como un diálogo entre obras de artistas argentinos contemporáneos que se vinculan a través de una sintaxis común entre ellas, una sintaxis basada en operaciones y estrategias de negación de la funcionalidad original de los objetos, funcionalidades que operan desde un modelo que nos fue inculcado, en donde los objetos nos deben servir, obedecer y ser bellos. Estos son objetos que luchan con su condición de modelo, su condición canónica o normativa, son al mismo tiempo reales y representados, objetos y cosas, estáticos y dinámicos. Son objetos que nos ofrecen una mirada sobre las transformaciones vividas dentro de la cultura material de la vida cotidiana. Ellos nos ayudan a reconocer ciertos aspectos concretos que distinguen la vida de las personas y a convertir las situaciones particulares y sociales en experiencias plausibles de ser narradas

Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

Sensor and feature selection for an emergency first responders activity recognition system

Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms

Using domain knowledge for interpretable and competitive multi-class human activity recognition

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems

Subject-dependent and -independent human activity recognition with person-specific and -independent models

The distinction between subject-dependent and subject-independent performance is ubiquitous in the Human Activity Recognition (HAR) literature. We test the hypotheses that HAR models achieve better subject-dependent performance than subject-independent performance, that a model trained with many users will achieve better subject-independent performance than one trained with a single user, and that one trained with a single user performs better for that user than one trained with this and other users by comparing four algorithms' subject-dependent and -independent performance across eight data sets using three different approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). Our analysis shows that PSMs outperform PIMs by 3.5% for known users, PIMs outperform PSMs by 13.9% and ensembles of PSMs by a not significant 2.1% for unknown users, and that the performance for known users is 20.5% to 48% better than for unknown users

A survey on the use of Artificial Intelligence for injury prediction in sports

Artificial Intelligence (AI) could play a significant role in injury prediction in sports due to its capabilities to detect and identify hidden patterns across multi-modal heterogeneous data sources. This paper aims at providing an up-to-date survey of the state-of-the-art in machine learning for injury predictions in sports. Finally, a number of considerations have been also drawn to discuss about the future research challenges required to be tackled to move this field forward

Monitoring emergency first responders' activities via gradient boosting and inertial sensor data

Emergency first response teams during operations expend much time to communicate their current location and status with their leader over noisy radio communication systems. We are developing a modular system to provide as much of that information as possible to team leaders. One component of the system is a human activity recognition (HAR) algorithm, which applies an ensemble of gradient boosted decision trees (GBT) to features extracted from inertial data captured by a wireless-enabled device, to infer what activity a first responder is engaged in. An easy-to-use smartphone application can be used to monitor up to four first responders' activities, visualise the current activity, and inspect the GBT output in more detail

Fragmentation of Andes-to-Amazon connectivity by hydropower dams

Andes-to-Amazon river connectivity controls numerous natural and human systems in the greater Amazon. However, it is being rapidly altered by a wave of new hydropower development, the impacts of which have been previously underestimated. We document 142 dams existing or under construction and 160 proposed dams for rivers draining the Andean headwaters of the Amazon. Existing dams have fragmented the tributary networks of six of eight major Andean Amazon river basins. Proposed dams could result in significant losses in river connectivity in river mainstems of five of eight major systems—the Napo, Marañón, Ucayali, Beni, and Mamoré. With a newly reported 671 freshwater fish species inhabiting the Andean headwaters of the Amazon (>500 m), dams threaten previously unrecognized biodiversity, particularly among endemic and migratory species. Because Andean rivers contribute most of the sediment in the mainstem Amazon, losses in river connectivity translate to drastic alteration of river channel and floodplain geomorphology and associated ecosystem services

Children’s self-reported discomfort of restorative treatments for deep caries lesions in primary teeth: results from a randomized clinical trial

The aim of this study was to evaluate the impact of different restorative techniques to treat deep caries lesions of primary molars on children’s self-reported discomfort. A randomized clinical trial with two parallel arms (1:1) was conducted in São Paulo, Brazil. 4-8 years-old children with at least one occlusal or occlusoproximal deep caries lesion in primary molars were selected. Molars were randomly allocated into two groups: (1) restoration performed with calcium hydroxide cement followed by high-viscosity Glass Ionomer Cement (CHC+HVGIC), and (2) HVGIC restoration. Immediately after the intervention, children reported the experienced discomfort during restoration to an external examiner using a Wong-Baker face-scale. Children’s self-reported discomfort was analyzed using Poisson regression comparing both groups and assessing other variables’ influence (α=5%). One hundred and eight children fulfilled the eligibility criteria and were randomized in the two groups (n=54). Most of the children who received CHC+HVGIC restorations reported none or minimal discomfort (83.3%). Similar scores (92.6%) were reported for those treated with HVGIC (p=0.758). The mean reported discomfort in children with CHC+HVGIC restorations was 0.37(1.01), and 0.41(1.01) for those with HVGIC restorations. Children’s self-reported discomfort was associated with age, sex, children’s cooperation, and intervention duration. We can conclude that CHC+HVGIC or HVGIC restorations result in none or minimal discomfort in the management of deep caries lesions, being considered a reliable option

The Risk of Contracting COVID-19 Is Not Increased in Patients With Celiac Disease

The World Health Organization declared coronavirus disease-2019 (COVID-19) a global pandemic in March 2020. Since then, there are more than 34 million cases of COVID-19 leading to more than 1 million deaths worldwide. Numerous studies suggest that celiac disease (CeD), a chronic immune-mediated gastrointestinal condition triggered by gluten, is associated with an increased risk of respiratory infections.1-3 However, how it relates to the risk of COVID-19 is unknown. To address this gap, we conducted a cross-sectional study to evaluate whether patients with self-reported CeD are at an increased risk of contracting COVID-19