Bibliografías temáticas / Human activity

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Literatura académica sobre el tema "Human activity"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Human activity"

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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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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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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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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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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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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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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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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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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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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’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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Tesis sobre el tema "Human activity"

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Albinali, Fahd. "Activity-Aware Computing: Modeling of Human Activity and Behavior." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/195382.

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With our society becoming increasingly mobile and devices that are small, inexpensive and wireless, we are transitioning from an age of desktop computing to an age where computers are used in all aspects of life and leisure. Ubiquitous Computing is largely concerned with the progression of computers from stationary desktop environments to environments where computers and sensors are integrated with objects and every aspect of our daily life, often in an invisible way.This dissertation investigates an important problem in Ubiquitous Computing: detecting domestic activities using ubiquitously de
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Reyes, Ortiz Jorge Luis. "Smartphone-based human activity recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/284725.

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Human Activity Recognition (HAR) is a multidisciplinary research field that aims to gather data regarding people's behavior and their interaction with the environment in order to deliver valuable context-aware information. It has nowadays contributed to develop human-centered areas of study such as Ambient Intelligence and Ambient Assisted Living, which concentrate on the improvement of people's Quality of Life. The first stage to accomplish HAR requires to make observations from ambient or wearable sensor technologies. However, in the second case, the search for pervasive, unobtrusive, low-p
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Outten, Alan Gerard. "Analysis of human muscle activity." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/7958.

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TOKALA, SAI SUJIT, and RANADEEP ROKALA. "HUMAN ACTIVITY MONITORING USING SMARTPHONE." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2566.

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The main aim of the project is to develop an algorithm which will classify the activity performed by a human who is carrying a smart phone. The day to day life made humans very busy at work and during daily activities, mostly elderly people who are at home have an important need to monitor their activity by others when they are alone, if they are inactive for a long time without movement, or in some situations like if they have fallen down, became unconscious for sometime or seized with a cardiac arrest etc… will help the observer to know the state of activity of person being monitored. In thi
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Ameri-Daragheh, Alireza. "Wearable human activity recognition systems." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1595755.

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<p> In this thesis, we focused on designing wearable human activity recognition (WHAR) systems. As the first step, we conducted a thorough research over the publications during the recent ten years in this area. Then, we proposed an all-purpose architecture for designing the software of WHAR systems. Afterwards, among various applications of these wearable systems, we decided to work on wearable virtual fitness coach device which can recognize various types and intensities of warm-up exercises that an athlete performs. We first proposed a basic hardware platform for implementing the WHAR softw
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Kepenekci, Burcu. "Human Activity Recognition By Gait Analysis." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613089/index.pdf.

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This thesis analyzes the human action recognition problem. Human actions are modeled as a time evolving temporal texture. Gabor filters, which are proved to be a robust 2D texture representation tool by detecting spatial points with high variation, is extended to 3D domain to capture motion texture features. A well known filtering algorithm and a recent unsupervised clustering algorithm, the Genetic Chromodynamics, are combined to select salient spatio-temporal features of the temporal texture and to segment the activity sequence into temporal texture primitives. Each activity sequence is repr
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Akpinar, Kutalmis. "Human Activity Classification Using Spatio-temporal." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614587/index.pdf.

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This thesis compares the state of the art methods and proposes solutions for human activity classification from video data. Human activity classification is finding the meaning of human activities, which are captured by the video. Classification of human activity is needed in order to improve surveillance video analysis and summarization, video data mining and robot intelligence. This thesis focuses on the classification of low level human activities which are used as an important information source to determine high level activities. In this study, the feature relation histogram based activi
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Qi, Lin. "Autonomous Identification of Human Activity Regions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212052.

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Human activity regions (HARs) are human-centric semantic partitions where observing and/or interacting with humans is likely in indoor environments. HARs are useful for achieving successful human-robot interaction, such as in safe navigation around a building or to know where to be able to assist humans in their activities. In this thesis, a system is designed for generating HARs automatically based on data recorded by robots. This approach to generating HARs is to cluster the areas that are commonly associated with frequent human presence. In order to detect human positions, we employ state-o
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Lakins, Johnathon N. "Structure and activity of human clusterin." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0021/NQ45178.pdf.

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Devaraj, Revathy. "Validation of the Human Activity Profile." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ52893.pdf.

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Libros sobre el tema "Human activity"

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Kawaguchi, Nobuo, Nobuhiko Nishio, Daniel Roggen, Sozo Inoue, Susanna Pirttikangas, and Kristof Van Laerhoven, eds. Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5.

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Pike, Graham. Human rights: Activity file. Thornes, 1990.

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Ahad, Md Atiqur Rahman, Upal Mahbub, and Tauhidur Rahman, eds. Contactless Human Activity Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4.

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Ahad, Md Atiqur Rahman, Paula Lago, and Sozo Inoue, eds. Human Activity Recognition Challenge. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8269-1.

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Pike, Graham. Human rights: Activity file. Stanley Thornes (Publishers) Ltd., 1991.

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Pike, Graham. Human rights: Activity file. Mary Glasgow, 1988.

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Naur, Peter. Computing, a human activity. ACM Press, 1992.

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Hodakov, Viktor. Natural environment and human activity. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194879.

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The monograph describes the influence of the natural environment and its natural and climatic conditions on human life and socio-economic systems, which are considered as regions, territories of Eastern Europe. The natural and climatic factors (PCFs) characterizing the natural environment of Eastern Europe (Russia and Ukraine) and Western (England and France) are considered. Eastern Europe is in the zone of negative PCFs, close to critical.&#x0D; The influence of the PCF on the vital activity of the state and man is systematically described: mentality, systemic thinking, human health, ensuring
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Hu, Zhongxu, and Chen Lv. Vision-Based Human Activity Recognition. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2290-9.

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Reyes Ortiz, Jorge Luis. Smartphone-Based Human Activity Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14274-6.

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Capítulos de libros sobre el tema "Human activity"

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Welle, Stephen. "Physical Activity." In Human Protein Metabolism. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1458-8_8.

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Komukai, Kohei, and Ren Ohmura. "Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_1.

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Lago, Paula, Shingo Takeda, Tsuyoshi Okita, and Sozo Inoue. "MEASURed: Evaluating Sensor-Based Activity Recognition Scenarios by Simulating Accelerometer Measures from Motion Capture." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_10.

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Wang, Lin, Hristijan Gjoreski, Mathias Ciliberto, Sami Mekki, Stefan Valentin, and Daniel Roggen. "Benchmark Performance for the Sussex-Huawei Locomotion and Transportation Recognition Challenge 2018." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_11.

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Osmani, Aomar, and Massinissa Hamidi. "Bayesian Optimization of Neural Architectures for Human Activity Recognition." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_12.

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Widhalm, Peter, Maximilian Leodolter, and Norbert Brändle. "Into the Wild—Avoiding Pitfalls in the Evaluation of Travel Activity Classifiers." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_13.

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Sloma, Michael, Makan Arastuie, and Kevin S. Xu. "Effects of Activity Recognition Window Size and Time Stabilization in the SHL Recognition Challenge." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_14.

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Janko, Vito, Martin Gjoreski, Gašper Slapničar, et al. "Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_15.

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Scholl, Philipp M., and Kristof Van Laerhoven. "Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_2.

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Nozaki, Junto, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. "Compensation Scheme for PDR Using Component-Wise Error Models." In Human Activity Sensing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_3.

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Actas de conferencias sobre el tema "Human activity"

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N, Gayathri, Tamilselvi S, Swetha G, Viddhya J, and Vidhya N. "Suspicious Human Activity Recognition." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716885.

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Malvi, Neeta B., Sahana B, and Mounith H. "Malicious Human Activity Detection." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816901.

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Wang, Yu, and Ana Basiri. "Advancing Human Activity Recognition Using Ultra-Wideband Channel Impulse Response Snapshots." In 2024 International Conference on Activity and Behavior Computing (ABC). IEEE, 2024. http://dx.doi.org/10.1109/abc61795.2024.10651886.

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Ge, Wen, Guanyi Mou, Emmanuel O. Agu, and Kyumin Lee. "Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition." In 2025 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2025. https://doi.org/10.1109/percom64205.2025.00039.

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Damarla, Thyagaraju, Lance Kaplan, and Alex Chan. "Human infrastructure & human activity detection." In 2007 10th International Conference on Information Fusion. IEEE, 2007. http://dx.doi.org/10.1109/icif.2007.4408122.

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Parmar, Divaksh, Mitanshu Bhardwaj, Aayush Garg, Anjali Kapoor, and Anju Mishra. "Human activity recognition system." In 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2023. http://dx.doi.org/10.1109/cictn57981.2023.10141250.

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Reid, Shane, Sonya Coleman, Dermot Kerr, Philip Vance, and Siobhan O’Neill. "Fast Human Activity Recognition." In International Conference on Image Processing and Vision Engineering. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010420300910098.

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Hung, Tzu-Yi, Jiwen Lu, Junlin Hu, Yap-Peng Tan, and Yongxin Ge. "Activity-based human identification." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638077.

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Gioanni, Luis, Christel Dartigues-Pallez, Stéphane Lavirotte, and Jean-Yves Tigli. "Opportunistic Human Activity Recognition." In MOBIQUITOUS 2016: Computing, Networking and Services. ACM, 2016. http://dx.doi.org/10.1145/2994374.3004075.

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Hu, Sha, Yu Gong, and Greg Mori. "Embodied Human Activity Recognition." In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2024. http://dx.doi.org/10.1109/wacv57701.2024.00632.

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Informes sobre el tema "Human activity"

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Griffith, J. Telomerase activity in human cancer. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/766184.

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Ali, Anjum, and J. K. Aggarwal. Segmentation and Recognition of Continuous Human Activity. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada396147.

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Flater, David, Philippe A. Martin, and Michelle L. Crane. Rendering UML activity diagrams as human-readable text. National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7469.

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Cheng, Zhiqing, Steve Mosher, Jeanne Smith, Isiah Davenport, John Camp, and Darrell Lochtefeld. Human Activity Modeling and Simulation with High Biofidelity. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada584135.

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Camp, John, Darrell Lochtefeld, Zhiqing Cheng, et al. Biofidelic Human Activity Modeling and Simulation with Large Variability. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada618197.

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Allen, Melissa R., H. M. Abdul Aziz, Mark A. Coletti, Joseph H. Kennedy, Sujithkumar S. Nair, and Olufemi A. Omitaomu. Workshop on Human Activity at Scale in Earth System Models. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1343540.

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Mocan, H. Naci, Stephen Billups, and Jody Overland. A Dynamic Model of Differential Human Capital and Criminal Activity. National Bureau of Economic Research, 2000. http://dx.doi.org/10.3386/w7584.

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Hristova, Marina, Plamen Todorov, Nadya Petrova, Diana Gulenova, Ibryam Ibryam, and Elena Hristova. Clonogenic Activity of Human Haematopoietic Stem Cells Cultured under Micro-vibrations. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.04.08.

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Matzen, Laura E., Michael Joseph Haass, Michael Christopher Stefan Trumbo, et al. Using recordings of brain activity to predict and improve human performance. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055638.

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Hu, Jieyun. Deep Learning in Indoor Human Activity Recognition with Millimeter Wave Radar. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-494.

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