Human Activity
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Sensor Review ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Gomathi V. ◽  
Kalaiselvi S. ◽  
Thamarai Selvi D

Purpose This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λmax method for weight initialization, as a data normalization technique, to achieve high accuracy of classification. Design/methodology/approach The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λmax method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λmax method. Findings The effectiveness of the proposed FDCNN model with λmax method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795. Practical implications The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λmax method. Social implications Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition. Originality/value A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

2022 ◽  
Vol 8 (1) ◽  
pp. 71-75
R. Orujeva

Gay gray-brown soils are distributed mainly in the foothills of the Lesser Caucasus, on the Ganja-Gazakh plain and in the lower reaches of the Araz basin. They are formed by changing volcanic rocks in hot and dry climates. In the process of erosion and soil formation, pyrite, alunitized and other sulfur-containing rocks turn into gazh, on which gray-brown gazh soils are formed. As a result of human activity, i. e. deep plowing and irrigation, these lands are being converted. It turned out that the transformation of these soils is clearly felt in the thickness of the humus layer, its distribution along the profile, quantity and composition. The thickness of the humus layer increases from 40–45 cm to 100 cm. As a result of the transformation, the length of the humus profile is constantly decreasing. Changes in the composition of humus led to an increase in the content of humic acids. Although the coefficient in the uncultivated area decreased from 1.36 to 0.80, in the irrigated area it changed from 1.70 to 0.93.

Jonatan Ginés Clavero ◽  
Francisco Martín Rico ◽  
Francisco J. Rodríguez-Lera ◽  
José Miguel Guerrero Hernandéz ◽  
Vicente Matellán Olivera

AbstractFacing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot’s behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones’ information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository ( and instructions to facilitate the reproducibility of the results.

Toxics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 35
Hugo Pérez ◽  
Gregorio Vargas ◽  
Rodolfo Silva

In humid environments, the formation of biofilms and microfouling are known to be the detrimental processes that first occur on stainless steel surfaces. This is known as biofouling. Subsequently, the conditions created by metabolites and the activity of organisms trigger corrosion of the metal and accelerate corrosion locally, causing a deterioration in, and alterations to, the performance of devices made of stainless steel. The microorganisms which thus affect stainless steel are mainly algae and bacteria. Within the macroorganisms that then damage the steel, mollusks and crustaceans are the most commonly observed. The aim of this review was to identify the mechanisms involved in biofouling on stainless steel and to evaluate the research done on preventing or mitigating this problem using nanotechnology in humid environments in three areas of human activity: food manufacturing, the implantation of medical devices, and infrastructure in marine settings. Of these protective processes that modify the steel surfaces, three approaches were examined: the use of inorganic nanoparticles; the use of polymeric coatings; and, finally, the generation of nanotextures.

Svanhild Breive

AbstractThis paper reports from a case study which explores kindergarten children’s mathematical abstraction in a teaching–learning activity about reflection symmetry. From a dialectical perspective, abstraction is here conceived as a process, as a genuine part of human activity, where the learner establishes “a point of view from which the concrete can be seen as meaningfully related” (van Oers & Poland Mathematics Education Research Journal, 19(2), 10–22, 2007, p. 13–14). A cultural-historical semiotic perspective to embodiment is used to explore the characteristics of kindergarten children’s mathematical abstraction. In the selected segment, two 5-year-old boys explore the concept of reflection symmetry using a doll pram. In the activity, the two boys first point to concrete features of the sensory manifold, then one of the boys’ awareness gradually moves to the imagined and finally to grasping a general and establishing a new point of view. The findings illustrate the essential role of gestures, bodily actions, and rhythm, in conjunction with spoken words, in the two boys’ gradual process of grasping a general. The study advances our knowledge about the nature of mathematical abstraction and challenges the traditional view on abstraction as a sort of decontextualised higher order thinking. This study argues that abstraction is not a matter of going from the concrete to the abstract, rather it is an emergent and context-bound process, as a genuine part of children’s concrete embodied activities.

The Holocene ◽  
2022 ◽  
pp. 095968362110665
Kevin Kearney ◽  
Benjamin Gearey ◽  
Susan Hegarty ◽  
Suzi Richer ◽  
Carla Ferreira ◽  

A multiproxy (pollen, microcharcoal, loss-on-ignition, magnetic susceptibility and geochemistry) sequence from Lough Cullin, southeast Ireland, supported by a high-resolution radiocarbon chronology, modelled using Bayesian approaches, provides a record of environmental change for much of the Holocene. Following the establishment of mixed deciduous woodland, climatic deterioration was likely responsible for pronounced vegetation change and erosion, 7615–6500 cal. BC to 6245–5575 cal. BC, evidence for the ‘8.2 Kyr’ BP climate event. The so-called ‘elm decline’ is dated to 4220–3980 cal. BC and whilst there are possible indications of an anthropogenic cause, clear evidence of woodland clearance with cereal pollen is recorded at 3900–3700 cal. BC, 3790–3580 cal. BC and 3760–3650 cal. BC, during a period of clearance and farming of 320–450 years duration. A reduction in farming/settlement and woodland regeneration during the Middle Neolithic parallels the archaeological record, with low levels of activity during the Late Neolithic/Chalcolithic after 2960–2525 cal. BC, prior to increases during the Bronze Age then woodland clearance and agriculture between 1500–1410 and 1275–1000 cal. BC, corresponding with the archaeological evidence. A subsequent ‘step-wise’ reduction in human activity follows, from the latter date to 815–685 cal. BC, and a brief but pronounced cessation at 690–535 cal. BC. Renewed woodland clearance and agriculture commenced until 415–250 cal. BC. From the latter date until cal. AD 390–540, the Late Iron Age/Early Medieval period, a phase of woodland recovery is attested, followed by renewed landscape disturbance and arable agriculture in particular, continuing to the close of the record at cal. AD 780–1035.

2022 ◽  
Vol 119 (3) ◽  
pp. e2110303118
Arlie H. McCarthy ◽  
Lloyd S. Peck ◽  
David C. Aldridge

Antarctica, an isolated and long considered pristine wilderness, is becoming increasingly exposed to the negative effects of ship-borne human activity, and especially the introduction of invasive species. Here, we provide a comprehensive quantitative analysis of ship movements into Antarctic waters and a spatially explicit assessment of introduction risk for nonnative marine species in all Antarctic waters. We show that vessels traverse Antarctica’s isolating natural barriers, connecting it directly via an extensive network of ship activity to all global regions, especially South Atlantic and European ports. Ship visits are more than seven times higher to the Antarctic Peninsula (especially east of Anvers Island) and the South Shetland Islands than elsewhere around Antarctica, together accounting for 88% of visits to Southern Ocean ecoregions. Contrary to expectations, we show that while the five recognized “Antarctic Gateway cities” are important last ports of call, especially for research and tourism vessels, an additional 53 ports had vessels directly departing to Antarctica from 2014 to 2018. We identify ports outside Antarctica where biosecurity interventions could be most effectively implemented and the most vulnerable Antarctic locations where monitoring programs for high-risk invaders should be established.

Family Forum ◽  
2022 ◽  
Vol 11 ◽  
pp. 15-34
Monika Joanna Kornaszewska-Polak

Abstract The idea of reconciling work with personal life was in its heyday at the turn of the 20th century when people realised that it was impossible to completely separate these inextricably linked spheres of human existence. Neglecting either of them, and not only in the scientific discourse but also in everyday life, is in many aspects detrimental to close relationships and to the performance at work. Nevertheless, a perfect combination of these two dimensions of human activity seems almost unattainable, as a growing number of contemporary studies show. Becoming involved in one entails some negligence in the other. The family context represents a relevant example of the attempts to reach the work-life balance. It is increasingly frequent that the contemporary young adults’ generation prioritise work, individual career, and personal development in their hierarchy of values. They delay their decisions on starting a family, having children, or simply settling down until they have achieved an adequate status and prosperity. Seeking to satisfy the need for close bonds, many young adults engage in only temporary relationships (cohabitation, swingers), but also create substitutes thereof. This generation succumbs to a growing sense of loneliness, despite the fulfilling careers or satisfying material and social statuses.

Anna Ferrari ◽  
Daniela Micucci ◽  
Marco Mobilio ◽  
Paolo Napoletano

AbstractHuman activity recognition (HAR) is a line of research whose goal is to design and develop automatic techniques for recognizing activities of daily living (ADLs) using signals from sensors. HAR is an active research filed in response to the ever-increasing need to collect information remotely related to ADLs for diagnostic and therapeutic purposes. Traditionally, HAR used environmental or wearable sensors to acquire signals and relied on traditional machine-learning techniques to classify ADLs. In recent years, HAR is moving towards the use of both wearable devices (such as smartphones or fitness trackers, since they are daily used by people and they include reliable inertial sensors), and deep learning techniques (given the encouraging results obtained in the area of computer vision). One of the major challenges related to HAR is population diversity, which makes difficult traditional machine-learning algorithms to generalize. Recently, researchers successfully attempted to address the problem by proposing techniques based on personalization combined with traditional machine learning. To date, no effort has been directed at investigating the benefits that personalization can bring in deep learning techniques in the HAR domain. The goal of our research is to verify if personalization applied to both traditional and deep learning techniques can lead to better performance than classical approaches (i.e., without personalization). The experiments were conducted on three datasets that are extensively used in the literature and that contain metadata related to the subjects. AdaBoost is the technique chosen for traditional machine learning, while convolutional neural network is the one chosen for deep learning. These techniques have shown to offer good performance. Personalization considers both the physical characteristics of the subjects and the inertial signals generated by the subjects. Results suggest that personalization is most effective when applied to traditional machine-learning techniques rather than to deep learning ones. Moreover, results show that deep learning without personalization performs better than any other methods experimented in the paper in those cases where the number of training samples is high and samples are heterogeneous (i.e., they represent a wider spectrum of the population). This suggests that traditional deep learning can be more effective, provided you have a large and heterogeneous dataset, intrinsically modeling the population diversity in the training process.

Jingfei Zhang ◽  
Biao Cai ◽  
Xuening Zhu ◽  
Hansheng Wang ◽  
Ganggang Xu ◽  

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