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Artículos de revistas sobre el tema "Human activity"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Sherief, Fathima, and Jogimol Joseph. "AI Based Human Activity Recognition." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1836–40. https://doi.org/10.21275/sr25418171724.

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2

Patel, Mayur A. "Combating Human Diseases through Physical Activity." Indian Journal of Applied Research 3, no. 2 (2011): 312–13. http://dx.doi.org/10.15373/2249555x/feb2013/106.

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3

L, Latha, Cynthia J, G. Seetha Lakshmi, Raajshre B, Senthil J, and Vikashini S. "Human Activity Recognition Using Smartphone Sensors." Webology 18, no. 04 (2021): 1499–511. http://dx.doi.org/10.14704/web/v18si04/web18294.

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In today’s digitalized world, smartphones are the devices which have become a basic and fundamental part of our life. Since, these greatest technology’s appearance, an uprising has been created in the industry of mobile communication. These greatest inventions of mankind are not just constricted for calling these days. As the capabilities and the number of smartphone users increase day by day, smartphones are loaded with various types of sensors which captures each and every moment, activities of our daily life. Two of such sensors are Accelerometer and Gyroscope which measures the acceleratio

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4

P, Anupriya, and Dr A. Mythili. "Human Activity Identification Using Image Processing." International Journal of Research Publication and Reviews 6, no. 4 (2025): 261–65. https://doi.org/10.55248/gengpi.6.0425.1313.

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5

Radhika, Omkar, and Shri N. Naveen Kumar. "Human Activity Recognition Using Cnn Techniques." International Journal of Research Publication and Reviews 6, no. 6 (2025): 12720–25. https://doi.org/10.55248/gengpi.6.0625.23110.

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6

Zhang, Tongda, Xiao Sun, Yueting Chai, and Hamid Aghajan. "Human Computer Interaction Activity Based User Identification." International Journal of Machine Learning and Computing 4, no. 4 (2014): 354–58. http://dx.doi.org/10.7763/ijmlc.2014.v4.436.

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7

Khupavtseva, Nataliia, and Liana Onufriieva. "Facilitative Interaction as a Multi-Level Human Activity." Collection of Research Papers "Problems of Modern Psychology" 59 (March 30, 2023): 73–95. http://dx.doi.org/10.32626/2227-6246.2023-59.73-95.

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Тhe purpose of our research is to show facilitative interaction as a multilevel human activity, to show the significant constructive phenomena of facilita-tive interaction as a psychological status of the individual. methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: a categorical method, structural and functional methods, the methods of the analysis, systematization, modeling, generalization. Also, in our research we used empirical methods, such as statement experiment.the results of the research. It was shown, t

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8

P. Ambiga, P. Ambiga, R. Bhavani R. Bhavani, P. Sivamani P. Sivamani, and R. R. Thanighai arassu. "Comparative Analysis of Microbial and Human Amylase Activity." Indian Journal of Applied Research 3, no. 3 (2011): 380–84. http://dx.doi.org/10.15373/2249555x/mar2013/130.

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9

Guda, B. B., V. V. Pushkarev, O. V. Zhuravel, et al. "Protein kinase Akt activity in human thyroid tumors." Ukrainian Biochemical Journal 88, no. 5 (2016): 90–95. http://dx.doi.org/10.15407/ubj88.05.090.

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10

Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.

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<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject&rsquo;s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to ext

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11

Chun-Mei Ma, Chun-Mei Ma, Hui Zhao Chun-Mei Ma, Ying Li Hui Zhao, Pan-Pan Wu Ying Li, Tao Zhang Pan-Pan Wu, and Bo-Jue Wang Tao Zhang. "Human Activity Recognition with Multimodal Sensing of Wearable Sensors." 電腦學刊 32, no. 6 (2021): 024–37. http://dx.doi.org/10.53106/199115992021123206003.

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12

Bobrovnik, S. A., M. A. Demchenko, and S. V. Komisarenko. "Age changes of human serum polyreactive immunoglobulins (PRIG) activity." Ukrainian Biochemical Journal 86, no. 5 (2014): 151–55. http://dx.doi.org/10.15407/ubj86.05.151.

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13

Dönmez, İlknur. "Human Activity Analysis and Prediction Using Google n-Grams." International Journal of Future Computer and Communication 7, no. 2 (2018): 32–36. http://dx.doi.org/10.18178/ijfcc.2018.7.2.516.

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14

KUZMINSKA, Natalia, and Olga KOZHEMIACHENKO. "IMPACT OF NATIONAL HUMAN CAPITAL ON POPULATION MIGRATION ACTIVITY." Herald of Khmelnytskyi National University. Economic sciences 312, no. 6(2) (2022): 283–86. http://dx.doi.org/10.31891/2307-5740-2022-312-6(2)-47.

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The article evaluates the national human capital as a factor in the migration activity of the population. For the study, data were taken from open international and national databases Ukraine, Republic of Moldova, the Russian Federation, Republic of Belarus, Republic of Poland. The assessment of the value of human capital was carried out using the indicators-based approach. All data were divided into four groups. Two groups characterize the costs of human capital formation (intelligence, knowledge and health). The other two groups characterize the conditions for obtaining income from human cap

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15

Maradani, Lohitha. "Human Activity Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1983–88. http://dx.doi.org/10.22214/ijraset.2022.45630.

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Abstract: Human Activity Recognition (HAR) is one of the active research areas in computer vision as well as human computer interaction. However, it remains a very complex task, due to unresolvable challenges such as sensor motion, sensor placement, cluttered background, and inherent variability in the way activities are conducted by different humans. Human activity recognition is an ability to interpret human body gesture or motion via sensors and determine human activity or action. Most of the human daily tasks can be simplified or automated if they can be recognized via HAR system. Typicall

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16

Kaewunruen, Sakdirat, Jessada Sresakoolchai, Junhui Huang, Satoru Harada, and Wisinee Wisetjindawat. "Human Activity Vibrations." Data 6, no. 10 (2021): 104. http://dx.doi.org/10.3390/data6100104.

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We present a unique, comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus, or a taxi. The measurements are carried out by embedded sensor accelerometers in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they perform the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertically stored in an Excel Macro-enabled Workbook (xlsm) format that can be used to train an

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17

Aggarwal, J. K., and M. S. Ryoo. "Human activity analysis." ACM Computing Surveys 43, no. 3 (2011): 1–43. http://dx.doi.org/10.1145/1922649.1922653.

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18

Ball, Philip. "Predicting human activity." Nature 465, no. 7299 (2010): 692. http://dx.doi.org/10.1038/465692a.

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19

Bais, Aniket R. "Human Activity Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41304.

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This research focuses on Human Activity Recognition (HAR) using sensor-based data, primarily from gyroscopes and accelerometers, to classify and analyze human movements in real time. With the increasing adoption of wearable devices and smart environments, HAR has gained significant importance in applications such as healthcare, fitness tracking, smart homes, and security systems. Traditional HAR methods relied on handcrafted features and classical machine learning algorithms, but recent advancements in deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (R

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20

Solomon, S., N. Muruganantham, and M. M. Senthamilselvi. "ANTICANCER ACTIVITY OF ABELMOSCHUS ESCULENTUS (FLOWERS) AGAINST HUMAN LIVER CANCER." International Journal of Pharmacy and Biological Sciences 6, no. 3 (2016): 154–57. http://dx.doi.org/10.21276/ijpbs.2016.6.3.18.

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21

Kumar Soni, Abhishikat, Dhruv Agrawal, Md Ahmed Ali, and B. G Prasad. "A Survey and High-Level Design on Human Activity Recognition." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1023–29. http://dx.doi.org/10.21275/sr22713132947.

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22

Kalachanis, Konstantinos. "Environment-Crisis and Human Activity." European Journal of Social Behaviour 1, no. 1 (2014): 10–13. https://doi.org/10.5281/zenodo.237042.

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Our attitude towards the Environment has been changed since Industrial Revolution because our civilization is characterized by the overconsumption of natural resources, resulting in the appearance on health problems, while millions of people have also condemned into poverty. In order to change this harmful model of economy, we need to change our way of thinking by realizing our unity with the Environment. In such a case, it is obvious that a new model of economic growth will arise, which is based on Sustainable Development. This type of development combines the protection of natural environmen

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23

Wong, James. "Human All too Human: Gossip as Activity." International Journal of the Humanities: Annual Review 2, no. 1 (2006): 0. http://dx.doi.org/10.18848/1447-9508/cgp/v02i01/42878.

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24

Dávalos, Patricia King. "Human Activity and Situation." Glimpse 15 (2014): 29–33. http://dx.doi.org/10.5840/glimpse2014157.

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25

Rai, R. K., Michael Balmer, Marcel Rieser, V. S. Vaze, Stefan Schönfelder, and Kay W. Axhausen. "Capturing Human Activity Spaces." Transportation Research Record: Journal of the Transportation Research Board 2021, no. 1 (2007): 70–80. http://dx.doi.org/10.3141/2021-09.

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26

Shukla, Harsh, and Meenu Pandey. "Human Suspicious Activity Recognition." International Innovative Research Journal of Engineering and Technology 5, no. 4 (2020): 14–17. http://dx.doi.org/10.32595/iirjet.org/v5i4.2020.130.

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27

Bhambri, Pankaj, Harpreet Kaur, Akarshit Gupta, and Jaskaran Singh. "Human Activity Recognition System." Oriental journal of computer science and technology 13, no. 0203 (2021): 91–96. http://dx.doi.org/10.13005/ojcst13.0203.05.

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Modern human activity recognAition systems are mainly trained and used upon video stream and images data that understand the features and actions variations in the data having similar or related movements. Human Activity Recognition plays a significant role in human-to-human and human-computer interaction. Manually driven system are highly time consuming and costlier. In this project, we aim at designing a cost-effective and faster Human Activity Recognition System which can process both video and image in order to recognize the activity being performed in it, thereby aiding the end user in va

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28

Guimarães, C. P., G. L. Cid, V. S. Santos, et al. "Human activity reference database." Work 41 (2012): 613–16. http://dx.doi.org/10.3233/wor-2012-0218-613.

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29

Thomas, E. L., R. I. Lehrer, and R. F. Rest. "Human Neutrophil Antimicrobial Activity." Clinical Infectious Diseases 10, Supplement 2 (1988): S450—S456. http://dx.doi.org/10.1093/cid/10.supplement_2.s450.

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30

Aloimonos, Y. "HAL: Human Activity Language." Journal of Vision 8, no. 6 (2010): 1050. http://dx.doi.org/10.1167/8.6.1050.

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31

Wojtyla, Aneta, Marta Gladych, and Blazej Rubis. "Human telomerase activity regulation." Molecular Biology Reports 38, no. 5 (2010): 3339–49. http://dx.doi.org/10.1007/s11033-010-0439-x.

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32

Giri, Pranit. "Human Activity Recognition System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6671–73. http://dx.doi.org/10.22214/ijraset.2023.53135.

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Abstract: Almost every university has its management system to manage the students' records. Currently, even though there is a student management system that manages the students' records in Universiti Malaysia Sarawak (UNIMAS), no permission is provided for lecturers to access the system. This is because the access permission is only to top management such as Deans and Deputy Deans of Undergraduate and Student Development due to its privacy setting. Thus, this project proposes a system named Student Performance Analysis System (SPAS) to keep track of students' results in the Faculty of Comput

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33

Dhivya, Karunya S., and Kumar Krishna. "Human Activity Recognition Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1024–28. https://doi.org/10.35940/ijeat.E9771.069520.

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Human action in a video based application plays a significant role that alerts the researchers towards recognizing the motion of human. Other video applications also have video content extraction, summarization, and human computer interactions. The existing methods needs manual footnote of pertinent portion of actions of our interest. Recognition of human action can be done authentic without physical commentary of applicable parts of action of any one’s interest. In this paper we try to update the previous reviews on many ways of recognizing Human activities in videos that had different

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34

Evander, Christian Dumalang, Davin Roberto, and . Lina. "Application of Human Activity Recognition in Supermarkets with Human Pose Estimation." Engineering and Technology Journal 9, no. 06 (2024): 4186–89. https://doi.org/10.5281/zenodo.11467240.

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Human Activity Recognition is the ability to interpret human body movements or movements through sensors and to determine human activities or actions. Most everyday human tasks can be simplified or automated if they can be identified through an activity management system. The introduction of human activities in supermarkets has many benefits in its development, such as Just Walk Out technologies such as Amazon Go and the prevention of theft of goods. Proposed research with the theme of human activity recognition that has a focus on supermarkets. The methods that will be used in this research a

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35

Lateef, Rana Abdulrahman, and Dr Ayad Rodhan Abbas. "A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers." Webology 19, no. 1 (2022): 1703–15. http://dx.doi.org/10.14704/web/v19i1/web19114.

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The wide use of smartphones and later smartwatches equipped with a set of sensors such as location, motion, and direction blaze the trail for researchers to better recognize human activity. However, researches on using inertial or motion sensors (i.e., accelerometer, gyroscope) for human activity recognition (HAR) has intensified and reside a great confrontation to be faced. Lately, many deep learning methods have been suggested to improve the human activity classification and discrimination performance to reach an optimal accuracy. Therefore, this paper applies a Convolutional eXtreme Gradien

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36

Lutsenko, T. N., M. V. Kovalenko, and O. Yu Galkin. "Validation of biological activity testing procedure of recombinant human interleukin-7." Ukrainian Biochemical Journal 89, no. 1 (2017): 82–89. http://dx.doi.org/10.15407/ubj89.01.082.

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37

Maria Anthony Kumar, L., S. Murugan, and A. Therasa Alphonsa. "Design of Improved Metaheuristics with Machine Learning Driven Human Activity Recognition." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 141–48. http://dx.doi.org/10.21275/sr23822120407.

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38

Skoliar, Y. N. "Онтологические истоки понимания и практическая деятельность человека". Вестник Вятского государственного университета, № 3(149) (12 січня 2024): 71–78. http://dx.doi.org/10.25730/vsu.7606.23.037.

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The article attempts to study human understanding, the object of which is practical activity, and which, within the framework of the activity approach, becomes a comprehensive strategy and is characterized by unique internal relations of the ontological sphere of the subject of action. The methodological basis of the research is philosophical approaches to defining the understanding of activity, starting with the writings of the ancient Greeks, where it is associated with wisdom, common sense and the solution of controversial problems, to the positions of classical philosophy, in which the und

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39

KOYAMA, Hikaru, Miki HAYAKAWA, and Norihiko MORIWAKI. "Human Big Data Sensor for Human Activity Measurement." Journal of the Society of Mechanical Engineers 117, no. 1150 (2014): 614–15. http://dx.doi.org/10.1299/jsmemag.117.1150_614.

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40

Abdul, Lateef Haroon P.S, and Eranna U. "A simplified machine learning approach for recognizing human activity." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3465–73. https://doi.org/10.11591/ijece.v9i5.pp3465-3473.

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With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system. Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performa

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41

Dogan, Gulustan. "Advances in Human Activity Recognition." Computer 54, no. 5 (2021): 4–6. http://dx.doi.org/10.1109/mc.2021.3055671.

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42

Kulminskaya, Anna A., Andrew N. Saveliev, and Kirill N. Neustroev. "Human Abzymes with Amylolytic Activity." Trends in Glycoscience and Glycotechnology 16, no. 87 (2004): 17–31. http://dx.doi.org/10.4052/tigg.16.17.

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43

Wojciechowska, Paulina. "Physical activity and human health." Medical Studies 4 (2014): 254–60. http://dx.doi.org/10.5114/ms.2014.47924.

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44

Jensen, H. S. "Elastolytic Activity of Human Monocytes." Scandinavian Journal of Rheumatology 22, no. 1 (1993): 48. http://dx.doi.org/10.3109/03009749309095113.

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45

Van der Spek, Stefan, Jeroen Van Schaick, Peter De Bois, and Remco De Haan. "Sensing Human Activity: GPS Tracking." Sensors 9, no. 4 (2009): 3033–55. http://dx.doi.org/10.3390/s90403033.

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46

atheed, mais irreem, Dena Ahmed, and Rashad Kamal. "Human Activity Recognition: literature Review." JOURNAL OF EDUCATION AND SCIENCE 30, no. 5 (2021): 12–29. http://dx.doi.org/10.33899/edusj.2021.130293.1162.

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47

Sevinç, Tuğba. "The Nature of Human Activity." Kilikya Felsefe Dergisi / Cilicia Journal of Philosophy 6, no. 2 (2019): 116–28. http://dx.doi.org/10.5840/kilikya20196213.

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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 elemen

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48

Roy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (2021): 6566. http://dx.doi.org/10.3390/s21196566.

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Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produc

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49

Jadhav Sarnaik, Neha Nilesh. "Human Activity Recognition using CNN." International Journal of Scientific and Research Publications (IJSRP) 10, no. 2 (2020): p9804. http://dx.doi.org/10.29322/ijsrp.10.02.2020.p9804.

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50

Xiang, Jianwen, Jing Tian, and Akira Mori. "Goal-directed human activity computing." Journal of Ambient Intelligence and Smart Environments 3, no. 2 (2011): 127–45. http://dx.doi.org/10.3233/ais-2011-0103.

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