scholarly journals Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

2019 ◽  
Vol 11 (18) ◽  
pp. 2091
Author(s):  
Liu ◽  
Du ◽  
Yi ◽  
Liang ◽  
Ma ◽  
...  
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Confusion Matrix ◽  
County Level ◽  
Location Service ◽  
Nighttime Lights ◽  
Positive Correlations ◽  
Log Linear ◽  
Activity Dynamics ◽  
Damage Estimates

The daily nighttime lights (NTL) and the amount of location-service requests (NLR) data have been widely used as a proxy for measures of disaster-induced power outages and geo-tagged human activity dynamics. However, the association between the two datasets is not well understood. In this study, we investigated how the NTL signals and geo-tagged human activities changed in response to Typhoon Mangkhut. The confusion matrix is constructed to quantify the changes of the NLR in response to Typhoon Mangkhut, as well as the changes of the NTL signals at the grid level. Geographically-weighted regression and quantile regression were used to examine the associations between the changes of the NTL and the NLR at both grid and county levels. The quantile regressions were also used to quantify the relationships between the dimmed NTL signals and the change of the NLR in disaster damage estimates at the county level. Results show that the percent of the grids with anomalous human activities is significantly correlated with the nearby air pressure and wind speed. Geo-tagged human activities varied in response to the evolution of Mangkhut with significant areal differentiation. Over 69.3% of the grids with significant human activity change is also characterized by declined NTL brightness, which is closely associated with abnormal human activities. Significant log-linear and moderate positive correlations were found between the changes of the NTL and NLR at both the grid and county levels, as well as between the county-level changes of NLR/NTL and the damage estimates. This study shows the geo-tagged human activities are closely associated with the changes of the daily NTL signals in response to Typhoon Mangkhut. The two datasets are complimentary in sensing the typhoon-induced losses and damages.

scholarly journals Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2237
Author(s):  
Umer Saeed ◽  
Syed Yaseen Shah ◽  
Syed Aziz Shah ◽  
Jawad Ahmad ◽  
Abdullah Alhumaidi Alotaibi ◽  
...  
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Daily Living ◽  
Continuous Wave ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Research Work ◽  
Heterogeneous Environments ◽  
Fmcw Radar ◽  
Unseen Data

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

scholarly journals Smartphone and Smartwatch for Human Activity Recognition

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mashhour M Bani Amer
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Elderly Care ◽  
Confusion Matrix ◽  
Ambient Assisted Living ◽  
Human Activity Recognition ◽  
Learning System ◽  
Support Vector ◽  
Close Monitoring

Human activity recognition (HAR) systems are developed as aspect of a model to allow continual assessment of human behaviors in IoT environments in the areas of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and close monitoring. Smartphones are already used to recognize activity. Most of the research done in this field placed a restriction on fixing the smartphone securely in a certain location on the human body, along with the machine learning system, to promote the process of classifying raw data from smartphone sensors to human activities. Smartwatches solve this limitation by placing them in a consistent position, which becomes steady and precisely sensitive to body movements. For this experiment, we evaluate both the accelerometer and the gyroscope sensor on the smartphone and the smartwatch, and decide which sensors hybrid does superiorly. Five daily physical human activities are evaluated using five classifiers from WEKA, in addition to Artificial Neural Network (ANN), K- Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms builtin MATLAB 2018a. We used confusion matrix and random simulation to compare the accuracy and efficiency of those models. The results showed that the accelerometer sensors combination has the highest accuracy among other combinations and achieved an overall accuracy of 97.7% using SVM that gives the best performance among all other classifiers.

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

scholarly journals SEA LEVEL CHANGES ALONG THE COASTS OF KEA ISLAND AND PALEOGEOGRAPHICAL COASTAL RECONSTRUCTION OF ARCHAEOLOGICAL SITES

2017 ◽  
Vol 43 (1) ◽  
pp. 453 ◽  
Author(s):  
N.D Mourtzas
Keyword(s):  
Sea Level ◽  
Human Activity ◽  
Human Activities ◽  
Archaeological Sites ◽  
Classical Period ◽  
Sea Level Changes ◽  
Coastal Morphology ◽  
Sea Levels

Sea level changes during the Upper Holocene submerged the coasts of Kea in three different phases about 5.50m, 3.90m and 1.50m respectively below the contemporary sea level thus causing sea transgression along the shores of Kea, which varied from 8m to 78m depending on the coastal morphology. These changes caused the alteration of the earlier morphology at coastal archaeological sites of the Island, as the prehistoric settlement of Ayia Irini and Classical period port of Karthaia, as well as, submerged under the sea areas of coastal human activity during antiquity, as the ancient schist quarry at Spathi bay. The study of historical, geomorphological and sedimentological data indicative of previous sea levels allow the paleogeographical reconstruction of the coasts during the period of human activities in these areas.

Excavations at an Iron Age Defended Enclosure at Tattershall Thorpe, Lincolnshire.

1986 ◽  
Vol 52 ◽  
pp. 159-188 ◽  
Author(s):  
Peter Chowne ◽  
Maureen Girling ◽  
James Greig
Keyword(s):  
Human Activity ◽  
Iron Age ◽  
Human Activities ◽  
Organic Material ◽  
Plant Macrofossil ◽  
Late Iron Age

Excavation of a late Iron Age enclosure at Tattershall Thorpe, Lincolnshire, produced substantial quantities of organic material preserved in the ditch filling. Insect, pollen and plant macrofossil remains allowed reconstruction of the environment and human activity in the area. Evidence for cultivation, grassland and human activities in the enclosure is discussed.

Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

2019 ◽  
Keyword(s):  
Land Use ◽  
Human Activity ◽  
Human Activities ◽  
Census Data ◽  
Lessons Learned ◽  
Data Sources ◽  
Aquatic Biota ◽  
Human Environment ◽  
Freshwater Habitats ◽  
Landscape Scales

<i>Abstract.</i>—Over the past decade, numerous studies have identified correlative relationships between aquatic biota and human activities at landscape scales. In addition to demonstrating the pervasive effects of these activities on aquatic biota, these findings have encouraged researchers to suggest that predictive relationships between human activities and aquatic biota could be used to enhance diagnostic power of biological assessments, predict future changes in species distributions, and inform land-use planning. However, to achieve these important goals, descriptions of human activities will need to become more detailed than the simple land use/land cover classifications frequently used. Our purpose is to highlight four sources of human activity data (existing geographic information system layers, census data, remotely sensed images, and visual landscape surveys) that can be used to increase the level of detail with which the human environment is described. Strengths and weaknesses of each data source are discussed and methods for adapting those data to aquatic studies are described by drawing on experiences from studies in the agricultural landscapes of southern Manitoba and southwestern Ontario, Canada. Based on the observations and lessons learned from our previous experiences, we make recommendations for how researchers can identify and apply the data sources that best meet their needs. We also discuss challenges and possible solutions for applying the described data sources as well as for improving data availability in the future. Moreover, we encourage aquatic researchers to allot more time to detailed description of human activities because we believe this to be an effective approach to improving our ability to predict the effects of human activity and thus better assist decision makers in protecting aquatic ecosystems.

Pascal on Happiness

2020 ◽  
Vol 32 (3) ◽  
pp. 8-19
Author(s):  
Michael Moriarty
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Motive Force ◽  
Underlying Condition ◽  
Authentic Happiness

Pascal sees happiness (bonheur) as the ultimate goal of all human activity, but argues that experience shows it to be unattainable; our underlying condition is unhappiness. In the immediate, he argues, human activities are forms of diversion or distraction, by which we seek to screen from ourselves our unhappiness and mortality and to gratify our vanity. This analysis omits the role of pleasure, which he elsewhere identifies as the motive force of all volition. In order to reconcile this anomaly, we need to distinguish between the motive of our actions, the ultimate end they have in view, and the Supreme Good. The motive of our actions is pleasure, their ultimate end happiness, and the Supreme Good God, in union with whom authentic happiness consists.

scholarly journals Invertebrates Diversity of Merta Segara and Nyangnyang Beach: Comparison Study of Two Beaches with Different Characteristics

2018 ◽  
Vol 2 (2) ◽  
pp. 14
Author(s):  
Buya Azmedia Istiqlal ◽  
I Wayan Kasa ◽  
Deny Suhernawan Yusup
Keyword(s):  
Community Structure ◽  
Human Activity ◽  
Human Activities ◽  
Tourism Industry ◽  
High Rate ◽  
Line Transect ◽  
Substrate Type ◽  
Invertebrate Diversity ◽  
Type Frequency ◽  
Different Characteristics

The diversity of intertidal invertebrates in Bali was believed to be affected by human activities, due to high rate of development in tourism industry. In order to reveal the real natural invertebrate diversity, it is necessary to investigate it at a kind of untouched beach of Bali. This study was perform by comparing invertebrate diversity (species richness, density, community structure) and human activity (Type, frequency) in Merta Segara Beach, as the beach with frequent human activities, and Nyangnyang Beach, as the beach with little human activities. Invertebrates sample were taken within intertidal zone using line transect-quadrate during low tide. Human activities were observed in the afternoon from March to April 2016. The result showed a significant different in density and community structure between both Merta Segara and Nyangnyang beach as the consequences of different substrate type of both beaches. Walking on substrate or trampling was believed to be the most influencing activity for invertebrate diversity, especially for Merta Segara Beach. Next, a thorough study must be performed to conclusively tie the human activity to the alteration of invertebrate diversity in a coastal area. The high diversity, density and abundance of intertidal invertebrate of Nyangnyang Beach has literally shown that how diverse the biodiversity could be if the beach were protected from overexploited by tourism visitation and activity.

The Nature of Human Activity

2019 ◽  
Vol 6 (2) ◽  
pp. 116-128
Author(s):  
Tuğba Sevinç ◽  
Keyword(s):  
Human Activity ◽  
Human Activities ◽  
Human Beings ◽  
Western Thought ◽  
History Of

In this work I present some of Arendt’s criticisms of Marx and assess whether these criticisms are fair. I claim that Arendt reads Marx erroneously, which results in her failure to grasp certain similarities between Marx and herself, at least on some points. It is important to mention that Arendt’s interest in Marx is part of a wider project she pursues. She believes that Marx’s theory might allow us to establish a link between Bolshevism and the history of Western thought. Marx’s notion of history and progress enables Arendt to support her claim that Marx’s theory involves totalitarian elements. By way of correcting Arendt’s misreading of Marx, my purpose has been to get a better understanding of the theories of Marx and Arendt, as well as to see their incompatible views regarding the nature of human activity and of freedom. Arendt charges Marx of ignoring the most central human activity, that is ‘action’; and of denying human beings a genuine political existence and freedom. Furthermore, according to Arendt, Marx conceives labor as human being’s highest activity and ignores the significance of other two activities, namely work and action. In the last analysis, Marx and Arendt prioritizes distinct human activities as the most central (labor and action, respectively) to human beings; and as a result, they provide us two irreconcilable views of politics, history and freedom.

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