Basic Human Activities Research Articles

Media dan Teknologi Pembelajaran PPKn Dalam Memenuhi Tujuan dan Fungsi Pendidikan Kewarganegaraan

Learning is a process of interaction that takes place consciously between teachers and students, who manage the environment (situation) so that students gain knowledge and knowledge, as well as the formation of attitudes among students. In other words, learning is a process where students learn well. The research method in this article is a qualitative research method with a literature study approach. Literature study is a method carried out by collecting and understanding data in literature. The results of this research are that digitalization of educational technology and learning media can be developed in the classroom in civic education learning. The conclusion from this research is that appropriate learning media really helps students in the teaching and learning process. With learning media, it can help teachers in providing explanations of the learning material that will be delivered. Apart from that, learning media also functions to help communication for both the communicator and the recipient. Digital technology has become an educational necessity nowadays, especially in Civics subjects because the Civics learning process is interpreted as a vehicle for the formation of identity and love for the homeland through internalization. or personalization of religious and cultural values which underlie human values, political values, educational science and technology values, artistic values, economic values and health values which are basic human activities in order to build better citizen insight.

Changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically — preliminary research

Introduction: Walking is one of the basic human activities. Taking care of the correct gait pattern is important for maintaining proper body posture and preventing dysfunctions of the musculoskeletal system. Since walking often involves carrying a load, the study aimed to evaluate changes in gait parameters and pelvic movement during gait under the influence of light loads carried symmetrically or asymmetrically in a bag or backpack. Material and methods: The group of 11 women and 15 men aged between 19 and 25 were examined. The BTS G-Sensor device was used. The basic gait parameters were examined, as well as the symmetry coefficient of pelvic motion and the range of pelvic motion in each of the three planes. The test involving walking 30 meters was repeated four times: while walking without a load, with an additional load worn in a backpack on both shoulders, in a bag on the right shoulder, or in a bag worn diagonally on the left shoulder. Each time the load was equal to 10% of the participantʼs body weight. Results: Walking speed, stride length to body height ratio, gait symmetry coefficient, and left and right foot propulsive speed did not change significantly during walking while carrying a load of 10% of body weight, regardless of the method of carrying a bag or backpack. Conclusions: Although carrying a light bag or backpack does not change the basic gait parameters, even such a small load reduces the symmetry of pelvic movement and the range of pelvic motion in the frontal and transverse planes during walking.

Household welfare improvement in the Mbulu district of Tanzania: does rural electrification matter?

Access to electricity is crucial for basic human activities and serves as a direct measure of energy poverty.

PENGEMBANGAN PENGINTEGRASIAN PENDIDIKAN LALU LINTAS DALAM MATA PELAJARAN PKN MELALUI MEDIA PEMBELAJARAN BERBASIS WEB UNTUK MENINGKATKAN KESADARAN HUKUM PESERTA DIDIK DI SMPN 5 BANDUNG

Teenage drivers are famous for unsafe and disorderly driving behavior, and a minimal level of awareness in traffic. In thiscase the researcher wants; 1) knowing the learning conditions of traffic education integration materials in Civic Educationsubjects at SMP Negeri 5 Bandung, 2) knowing how to develop web-based learning media for conceptual education of traffic education materials in Civics subjects, 3) knowing how to implement learning-based media web in conceptual integration of traffic education material in Civics subjects, 4) knowing the increase of students' legal awareness after theapplication of web-based learning model in the material of integrating traffic education in Civics subjects. Somantri (Wuryan and Syaifullah, 2009.76) interpreted Civics as a selection, adaptation from cross-disciplines of social sciences, citizenship, humanities, technology, religion, basic human activities organized and presented psychologically andscientifically. to participate in achieving one of the objectives of social science education and national education goals. The approach used by researchers is the Research and Development (R & D) approach. By using data collectiontechniques, including observation, interviews, questionnaires, literature studies, documentation studies. The population used is the population of SMPN 5 Bandung. The research samples were: class VII-A, class VII-B, class VII-C, class VII-D, and class VII-E. Analysis of his research uses qualitative analysis and quantitative analysis. The results showed that; 1) the condition of learning the integration of traffic education in Civics subjects is still conservative, especially in theuse and use of learning media, resulting in students being less interested in learning material for integrating traffic education in Civics subjects, 2) producing web-based learning media that in accordance with the purpose of its manufacture, the stages that must be carried out by the media developer are starting from the preproduction, productionand post-production stages. To be able to run this web-based learning media does not require special skills in learning, only basic computer skills are needed for teachers and students because this web-based learning media usescomputer media and internet connection, 3)web-based learning media contributes significantly to increasing legal awareness student traffic in learning material integrates traffic education in Civics subjects and 4)contributes significantly to improving student learning outcomes.

Research on Value Evaluation and Impact Mechanism of Water Ecological Services in Mountainous Cities: A Case Study of Xiangxi Prefecture

To have a more comprehensive understanding of the water ecological services of Xiangxi, the index system of water ecological service is constructed and the intensity of its influencing factors founded on the geographic detector model (GDM) is obtained in this paper. Then, the water ecological service index of eight cities in Xiangxi to evaluate the function level is determined. It is proved that: (a) The overall service value of water cultural function as a key factor in the construction of water ecological services, soil conservation value, and water supply value are relatively poor. (b) The value of the water ecological culture function type is the largest, with an overall value of 353.32 billion yuan. The value of water resources supply is the smallest, with a value of only 4.37 billion yuan, which indicates that human activities have a strong interference with the service function of the water ecosystem. (c) Based on GDM, four factors, including precipitation, resident population, sewage discharge, and water quality compliance rate, have a significant impact on water ecological services. Among them, per capita GDP(X7) > vegetation coverage (X6) > precipitation (X2) > resident population (X4), which further clearly explains the impact intensity of mountain climate basic conditions and human socio-economic activities on water ecological services. The results can offer a practical basis for the spatial development and protection of the water ecosystem to coordinate humans and the environment.

KAJIAN ILMU PENGETAHUAN SOSIAL DAN ILMU SOSIAL SEBAGAI BAHAN MATERI IPS UNTUK SEKOLAH DASAR

Social studies and social sciences are interconnected. Both are related to basic human needs, then these basic needs can be achieved by basic human activities. Basic human activities include education, production and consumption, organization and government, protection and maintenance, aesthetics and recreation. All of them build social sciences. In the social sciences, it is classified into fields of science which include, among others, history, economics, political science, law, geography, anthropology, and social psychology. And in it there are facts, concepts, generalizations that were developed to build Social Sciences (IPS).

A comprehensive ultra-wideband dataset for non-cooperative contextual sensing

Nowadays, an increasing amount of attention is being devoted towards passive and non-intrusive sensing methods. The prime example is healthcare applications, where on-body sensors are not always an option or in other applications which require the detection and tracking of unauthorized (non-cooperative) targets within a given environment. Therefore, in this paper we present a dataset consisting of measurements obtained from Radio-Frequency (RF) devices. Essentially, the dataset consists of Ultra-Wideband (UWB) data in the form of Channel Impulse Response (CIR), acquired via a Commercial Off-the-Shelf (COTS) UWB equipment. Approximately 1.6 hours of annotated measurements are provided, which are collected in a residential environment. This dataset can be used to passively track a target’s location in an indoor environment. Additionally, it can also be used to advance UWB-based Human Activity Recognition (HAR) since three basic human activities were recorded, namely, sitting, standing and walking. We anticipate that such datasets may be utilized to develop novel algorithms and methodologies for healthcare, smart homes and security applications.

‘인간적’ 조직과 ‘인간의 조건’: 아렌트(Arendt)의 ‘활동적 삶’에 대한 통찰

With the rising distrust in Capitalism and despair over corporations, many firms have implemented a variety of changes wishing to become “human” organizations. In response, this essay questions whether we really have a way of thinking that is required to build such human organizations. The failures of corporations are due at least in part to the failures of mainstream economics and the failures of business administration. In order to address the issues of corporate distrust, it may be necessary to search for new wisdom from other sources outside of business. Philosophy is one such field from which inspiration can be drawn. Indeed, this essay draws upon Arendt’s philosophical insights into the human existential condition and the fundamental forms of “vita activa” to understand the possibility of “human” organizations given the relationship between corporations and their employees. It first presents Arendt’s typology of the three basic human activities, namely labor, work, and action, as developed in her book The Human Condition. It then describes the reality of the one-sided dominance of labor over the other two activities. Next, this essay addresses the necessity and the possibility for the restoration of work and action in organizations, using teams as a contextual example. It concludes with discussion of comprehensive implications.

Research Progress on Triboelectric Nanogenerator for Sports Applications

Progress in science and technology drives the continuous innovation of energy collection and utilization. In the field of sports, the information collection and analysis based on Internet of things have attracted particular attention. Moreover, triboelectric nanogenerator (TENG) has promising applications in the field of sports. Here, we introduce the working principle of the TENG then the progress of the TENG as a wearable energy sensor is examined in the two fields of basic human activities and sports, especially competitive sports. On this basis, it is considered that the stability of devices, the universality of materials, and the scientificity of application of the TENG in the future need to be improved. We provide a direction for further upgrading energy collection technology to promote the high-quality development of human mechanical energy sensing in the field of sports.

Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex human activities. Recognition of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart watches. Other reasons include lack of sensor-based labeled dataset having several complex human daily life activities. Some of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition, whereas a few of them used both pocket and wrist positions. In this research, we have proposed a novel framework which is capable to recognize both basic and complex human activities using built-in-sensors of smart phone and smart watch. We have considered 25 physical activities, including 20 complex ones, using smart device’s built-in sensors. To the best of our knowledge, the existing literature consider only up to 15 activities of daily life.

Impact of personal protective equipment on psychological and physical health during menstruation: A cross-Sectional study among female frontline healthcare workers during COVID pandemic.

During the Coronavirus disease 2019 (COVID-19) pandemic, personal protective equipment (PPE) has undoubtedly provided a shield of protection for the healthcare workers fighting the disease. However, the layers of the PPE kit restrict basic human activities and also have been associated with various other problems like dehydration, suffocation, breathlessness, headache, and dermatitis. A cross-sectional study was done among 180 Female health workers who wore the PPE kit during COVID-19 duty in a tertiary health care center during the period March 2021 to May 2021. The content-validated survey tool developed by Google Forms used as a platform was sent to 180 Female health workers of COVID-19 hospitals to minimize face-to-face or physical interaction. The Work-Related Quality of Life Scale (WRQoL) was used. The majority of the participants were interns (48.9%) and belonged to the age group of 21 to 25 years (67.7%). The approximate duration of wearing a PPE kit was 6 to 12 h for almost half of the study participants. Sanitary pads were used by over 90% of the participants. Only 59 out of 180 (32.7%) were provided with an extra PPE kit during menstruation. About 131 (72.7%) Female Health workers (FHWs) (FHWs) had to remove the PPE kit due to excessive sweat during menstruation; 110 FHW's out of 180 (61.1%) who participated in the study had to remove the PPE kit due to severe thirst and dehydration. Sweating (91%), dehydration (86.7%), rashes (49.9%), extreme thirst (56.5%), abdominal cramps (53.3%), and headache (59.8%) were the major complaints of the participants. The majority of the participants had low quality of life scores based on WRQoL. Use of PPE for long hours especially during menstruation can affect one both physically and psychologically compromising the work performance and decision-making. Minor changes in the work place environment can drastically improve the work-related quality of life.

Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network

Human Activity Recognition (HAR) has become an active field of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneficial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efficient model for such datasets and some futuristic ideas pose open challenges in the research community.

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities.

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

Detecting basic human activities and postural transition using robust machine learning techniques by applying dimensionality reduction methods

In the whole world, approximately two billion people are using smartphones for their better life The smartphone’s sensor data consist of high dimensions which increase complexity of training model and degrade overall performance of a classifier. We investigated and analyzed the performance of different machine learning algorithms with various dimensionality reduction techniques on publicly available smartphone-based recognition of human activities and postural transitions dataset. The highest accuracy was obtained at set 1 using support vector machine (SVM) linear kernel (97.40%) followed by set 3 using eXtreme boosting (XGBoost) (96.92%), set 2 using adaptive boosting multi-class (AdaBoost.M1) (95.69%), set 4 using AdaBoost. M1 (82.17%). Using different dimensionality reduction methods for set 1, principal component analysis (PCA) provided the highest accuracy using average neural network (AvNNET) (98.27%), while chi-squared gives the accuracy of 93.72% using the XGBoost classifier. For set 2, PCA gives the highest accuracy of 95.83% using the AvNNET classifier while for set 3 Chi-squared gives an accuracy of 84.46% using average neural network (AvNNET), SVM.L, and stochastic Gradient Boosting Machine (GBM) classifiers. The results reveal that overall dimensionality reduction methods outer performed. The outcomes will be helpful in making wise decisions in detecting various activities in anti-terrorists, surveillance, etc.

How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

The COVID-19 pandemic has changed human daily activities significantly. Understanding the nature, causes, and extent of these changes is essential to evaluate the pandemic's influence on commerce, transportation, employment, and environment, among others. However, existing studies mainly focus on changes to general human mobility patterns; few have investigated changes in specific human daily activities. Based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, we tracked intensity changes in two basic human daily activities, dwelling and working, over the stages of COVID-19. The results show that during COVID-19 outbreak, human working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas. After COVID-19 was under control, intensity in most regions has recovered, but that in schools, hotels, entertainment venues, and tourism areas has not. These intensity changes at regional scale are due to behavior changes at individual scale: about 43% of residents left Beijing before COVID-19, while only 16% have returned back; all commuters decreased their commuting times during COVID-19, while only 75% have reverted to normal. The findings reveal variations in human activities caused by COVID-19 that can support targeted urban management in the post-epidemic era.

Influence of pore morphologies on the mechanical and tribo-electrical performance of polydimethylsiloxane sponge fabricated via commercial seasoning templates

This work demonstrated the influence of pore morphologies on the mechanical behavior and tribo-electrical performance of fabricated polydimethylsiloxane (PDMS) sponge. Commercial seasonings with different 3D geometric shapes were used as a sacrificial template to control the pore structure of the PDMS sponge. The result indicated that the softest PDMS sponge was molded by using a sodium chloride (NaCl) crystal template, as indicated by the lowest compressive modulus value. Then, P(VDF–HFP) was incorporated into PDMS prepolymer in order to enhance the charge generation characteristic of PDMS. Besides, the composite 3D structure was revealed using synchrotron radiation X-ray tomographic microscopy (SRXTM). Interpretation from the SRXTM result confirmed that the porous structure had different pore shapes, i.e., an octahedral-like shape and a circular-like shape in a particular sponge. By pairing the composite PDMS sponge with an aluminum (Al) plate for the triboelectric nanogenerator (TENG), the maximum electrical outputs of ~29.9 V and ~0.56 μA for voltage and current, respectively, were detected with loading 50 wt% of P(VDF – HFP). The presented TENG was applied successfully for sensing basic human activities practically, which demonstrated potential applications in wearable electronics.

Asymmetric Gait Analysis Using a DTW Algorithm with Combined Gyroscope and Pressure Sensor.

Walking is one of the most basic human activities. Various diseases may be caused by abnormal walking, and abnormal walking is mostly caused by disease. There are various characteristics of abnormal walking, but in general, it can be judged as asymmetric walking. Generally, spatiotemporal parameters can be used to determine asymmetric walking. The spatiotemporal parameter has the disadvantage that it does not consider the influence of the diversity of patterns and the walking speed. Therefore, in this paper, we propose a method to analyze asymmetric walking using Dynamic Time Warping (DTW) distance, a time series analysis method. The DTW distance was obtained by combining gyroscope data and pressure data. The experiment was carried out by performing symmetrical walking and asymmetrical walking, and asymmetric walking was performed as a simulation of hemiplegic walking by fixing one ankle using an auxiliary device. The proposed method was compared with the existing asymmetric gait analysis method. As a result of the experiment, a p-value lower than 0.05 was obtained, which proved that there was a statistically significant difference.

STATISTICAL UNCERTAINTY IN DROUGHT FORECASTING USING MARKOV CHAINS AND THE STANDARD PRECIPITATION INDEX (SPI).

Droughts affect basic human activities, and food and industry production. An adequate drought forecasting is crucial to guarantee the survival of population and promote societal development. The Standard Precipitation Index (SPI) is recommended by the World Meteorological Organization (WMO) to monitor meteorological drought. Using drought classification based on SPI to build Markov chains is a common tool for drought forecasting. However, Markov chains building process produce uncertainties inherent to the transition probabilities estimation. These uncertainties are often ignored by practitioners. In this study we analyze the statistical uncertainties of using Markov chains for drought annual forecasting. As a case study, the dry region of the State of Ceará (Northeastern Brazil) is analyzed, considering the precipitation records from 1911 to 2019. In addition to 100-year database (1911-2011) for Markov chain modeling and 8-year data (2012-2019) for forecasting validation, four fictional database extensions were considered in order to assess the effect of database size in the uncertainty. A likelihood ratio is used as index to assess model performance. The uncertainties assessment showed that an apparent performant Markov chain model for drought class forecasting may not be more informative than the historic proportion of drought class. Considering these uncertainties is crucial for an adequate forecasting with Markov chains.

Concept of indecisiveness within the HEXACO personality framework.

Decision making is one of the basic human activities. Indecisiveness, i.e. the stable tendency towards not making decisions in a timely manner, may influence the quality and speed in decision making and have long-term consequences for our professional and personal life. The goal of this research was to examine the position of indecisiveness within the HEXACO model of personality, at both the broad domains and narrow facets level. A secondary goal was to translate the Frost & Shows Indecisiveness Scale (IS) into the Croatian language and examine its validity and reliability. An internet-based sample size of 296 participants filled in both short and long version of Indecisiveness Scale and the HEXACO-PI-R. The results show that the IS retains the original one-factor structure as proposed by the original authors and has a high reliability. The shortened version shows properties very similar to the longer version and is deemed an appropriate replacement. Extraversion showed the highest positive correlation with indecisiveness, followed by negative correlations with conscientiousness, emotionality and agreeableness. The Croatian translation of the IS is a valid and reliable measure. The results also confirm that the shortened 11-item version can be used as a replacement for the full 15-item version. Since there was no measure of indecisiveness in Croatian before, this instrument could be used in the future by researchers interested in this construct. It was shown that extraversion has the strongest relationship with indecisiveness, followed by conscientiousness, emotionality and agreeableness.

Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model.

Human activity recognition (HAR) has been a vital human–computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodology equipped with low-cost sensors is proposed for high-accuracy device-free human activity recognition. ESP8266, as the sensing hardware, was utilized to deploy the WiFi sensor network and collect multi-dimensional received signal strength indicator (RSSI) records. The proposed learning model presents a coarse-to-fine hierarchical classification framework with two-level perception modules. In the coarse-level stage, twelve statistical features of time–frequency domains were extracted from the RSSI measurements filtered by a butterworth low-pass filter, and a support vector machine (SVM) model was employed to quickly recognize the basic human activities by classifying the signal statistical features. In the fine-level stage, the gated recurrent unit (GRU), a representative type of recurrent neural network (RNN), was applied to address issues of the confused recognition of similar activities. The GRU model can realize automatic multi-level feature extraction from the RSSI measurements and accurately discriminate the similar activities. The experimental results show that the proposed approach achieved recognition accuracies of 96.45% and 94.59% for six types of activities in two different environments and performed better compared the traditional pattern-based methods. The proposed hierarchical learning method provides a low-cost sensor-based HAR framework to enhance the recognition accuracy and modeling efficiency.