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Готові списки джерел за темами / Human activity

Добірка наукової літератури з теми "Human activity"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 11 вересня 2023

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Статті в журналах з теми "Human activity":

1

Patel, Mayur A. "Combating Human Diseases through Physical Activity." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 312–13. http://dx.doi.org/10.15373/2249555x/feb2013/106.

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2

L, Latha, Cynthia J, G. Seetha Lakshmi, Raajshre B, Senthil J, and Vikashini S. "Human Activity Recognition Using Smartphone Sensors." Webology 18, no. 04 (September 28, 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 acceleration and angular velocity respectively. These could be used to identify the human activities performed. Basically, Human Activity Recognition is a classifying activity with so many use cases such as health care, medical, surveillance and anti-crime securities. Smartphones have wide variety of applications in various fields and can be used to excavate different kinds of data which provide accurate insights and knowledge about the user's lifestyle. Nowadays creating lifelogs that is a technology to capture and record a user's life through his or her mobile devices, are becoming very important task. An immense issue in creating a detailed lifelog is the accurate detection of activities performed by human based on the collected data from the sensors. The data in the lifelogs has strong association with physical health variables. These data are motivational and they identify any type of behavioral changes. These data provide us the overall measure of physical activity. In this project, we have analyzed the smartphone sensors produced data and used them to recognize the activities performed by the user.

3

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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4

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 (October 1, 2011): 380–84. http://dx.doi.org/10.15373/2249555x/mar2013/130.

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5

Guda, B. B., V. V. Pushkarev, O. V. Zhuravel, A. Ye Kovalenko, V. M. Pushkarev, Y. M. Taraschenko, and M. D. Tronko. "Protein kinase Akt activity in human thyroid tumors." Ukrainian Biochemical Journal 88, no. 5 (October 31, 2016): 90–95. http://dx.doi.org/10.15407/ubj88.05.090.

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6

Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (June 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 extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>

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, that the concept “facilitation” reflects a conscious and purposeful activity as a phenomenon characteristic, first of all, of a teacher. Thus, we singled out the attributes of facilitation: 1) cogni-tive activity; 2) the subject of the activity; 3) the functions of the subject; 4) the object of the activity; 5) the motives of the activity; 6) the purpose of the activity; 7) functions of the activity; 8) the ways of performing activities; 9) methods of activity implementation (and means relevant for the implementation of these activities); 11) the result of the activity.conclusions. We showed the characteristics of facilitative interaction. We proved, that the Activity was the basis, means and positive condition for the development of the Personality. The Activity is the expedient transformation of the surrounding reality of people. We call activity “a unit of life”, mediated by the process of mental reflection. Also, outside activity there are neither means of the activity, nor signs, nor objects of art; there are no people outside the activity.Therefore, the activity is a purposeful, multilevel human activity. “Pur-poseful” is because “the subject” appears as its goal. “Multilevel” is because it includes into its structure of actions, secondary motivation, determined by the purpose and the tasks of the activity. And this, in turn, ensures the actualization of the main goalmotive of the activity by the individual. And, finally, the opera-tion of the activity differs from the action in that it is not marked by a goal, but by the conditions of the activity in which this goal is explained. It is very necessary to distinguish the actions from the activities and from operations.

8

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 (December 2021): 024–37. http://dx.doi.org/10.53106/199115992021123206003.

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9

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 (October 27, 2014): 151–55. http://dx.doi.org/10.15407/ubj86.05.151.

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10

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

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Дисертації з теми "Human activity":

1

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 deployed sensors from data sets of limited size. The dissertation assumes that home environments in the next 20 years will support a wide range of sensing technologies that are built in smart appliances and the surrounding environment (e.g. RFID tags and readers, accelerometers, temperature sensors etc.). The dissertation also assumes that there will be an abundance of embedded CPU power in the environment that will enable fast and efficient spectral analysis and feature extraction from sensor signals. Using efficient wireless technologies such as the new Bluetooth Wibree protocol, these devices will be able to communicate their sensed data in an efficient way.Two approaches are presented for domestic activity recognition from wireless sensors. The first approach is rule-based and logical in nature and is suitable when sensor data is not present for training. Importantly, fuzzy distributions model the uncertainty and variability in expert knowledge. The second approach is probabilistic in nature and learns by observation without human intervention. This approach uses Bayesian Learning and is optimized to deal with sparse data sets (with hundreds of sensor readings and few instances of activities). Further, a case study is presented in which activity recognition optimizes energy consumption for wireless PC cards that results in significant energy savings.This dissertation concludes by highlighting major and minor results. A summary of the author's future and current research efforts is presented including the application of activity recognition in medical interventions and resource allocation problems.

2

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-powered, and low-cost devices for achieving this challenging task still has not been fully addressed. In this thesis, we explore the use of smartphones as an alternative approach for performing the identification of physical activities. These self-contained devices, which are widely available in the market, are provided with embedded sensors, powerful computing capabilities and wireless communication technologies that make them highly suitable for this application. This work presents a series of contributions regarding the development of HAR systems with smartphones. In the first place we propose a fully operational system that recognizes in real-time six physical activities while also takes into account the effects of postural transitions that may occur between them. For achieving this, we cover some research topics from signal processing and feature selection of inertial data, to Machine Learning approaches for classification. We employ two sensors (the accelerometer and the gyroscope) for collecting inertial data. Their raw signals are the input of the system and are conditioned through filtering in order to reduce noise and allow the extraction of informative activity features. We also emphasize on the study of Support Vector Machines (SVMs), which are one of the state-of-the-art Machine Learning techniques for classification, and reformulate various of the standard multiclass linear and non-linear methods to find the best trade off between recognition performance, computational costs and energy requirements, which are essential aspects in battery-operated devices such as smartphones. In particular, we propose two multiclass SVMs for activity classification:one linear algorithm which allows to control over dimensionality reduction and system accuracy; and also a non-linear hardware-friendly algorithm that only uses fixed-point arithmetic in the prediction phase and enables a model complexity reduction while maintaining the system performance. The efficiency of the proposed system is verified through extensive experimentation over a HAR dataset which we have generated and made publicly available. It is composed of inertial data collected from a group of 30 participants which performed a set of common daily activities while carrying a smartphone as a wearable device. The results achieved in this research show that it is possible to perform HAR in real-time with a precision near 97\% with smartphones. In this way, we can employ the proposed methodology in several higher-level applications that require HAR such as ambulatory monitoring of the disabled and the elderly during periods above five days without the need of a battery recharge. Moreover, the proposed algorithms can be adapted to other commercial wearable devices recently introduced in the market (e.g. smartwatches, phablets, and glasses). This will open up new opportunities for developing practical and innovative HAR applications.
El Reconocimiento de Actividades Humanas (RAH) es un campo de investigación multidisciplinario que busca recopilar información sobre el comportamiento de las personas y su interacción con el entorno con el propósito de ofrecer información contextual de alta significancia sobre las acciones que ellas realizan. Recientemente, el RAH ha contribuido en el desarrollo de áreas de estudio enfocadas a la mejora de la calidad de vida del hombre tales como: la inteligència ambiental (Ambient Intelligence) y la vida cotidiana asistida por el entorno para personas dependientes (Ambient Assisted Living). El primer paso para conseguir el RAH consiste en realizar observaciones mediante el uso de sensores fijos localizados en el ambiente, o bien portátiles incorporados de forma vestible en el cuerpo humano. Sin embargo, para el segundo caso, aún se dificulta encontrar dispositivos poco invasivos, de bajo consumo energético, que permitan ser llevados a cualquier lugar, y de bajo costo. En esta tesis, nosotros exploramos el uso de teléfonos móviles inteligentes (Smartphones) como una alternativa para el RAH. Estos dispositivos, de uso cotidiano y fácilmente asequibles en el mercado, están dotados de sensores embebidos, potentes capacidades de cómputo y diversas tecnologías de comunicación inalámbrica que los hacen apropiados para esta aplicación. Nuestro trabajo presenta una serie de contribuciones en relación al desarrollo de sistemas para el RAH con Smartphones. En primera instancia proponemos un sistema que permite la detección de seis actividades físicas en tiempo real y que, además, tiene en cuenta las transiciones posturales que puedan ocurrir entre ellas. Con este fin, hemos contribuido en distintos ámbitos que van desde el procesamiento de señales y la selección de características, hasta algoritmos de Aprendizaje Automático (AA). Nosotros utilizamos dos sensores inerciales (el acelerómetro y el giroscopio) para la captura de las señales de movimiento de los usuarios. Estas han de ser procesadas a través de técnicas de filtrado para la reducción de ruido, segmentación y obtención de características relevantes en la detección de actividad. También hacemos énfasis en el estudio de Máquinas de soporte vectorial (MSV) que son uno de los algoritmos de AA más usados en la actualidad. Para ello reformulamos varios de sus métodos estándar (lineales y no lineales) con el propósito de encontrar la mejor combinación de variables que garanticen un buen desempeño del sistema en cuanto a precisión, coste computacional y requerimientos de energía, los cuales son aspectos esenciales en dispositivos portátiles con suministro de energía mediante baterías. En concreto, proponemos dos MSV multiclase para la clasificación de actividad: un algoritmo lineal que permite el balance entre la reducción de la dimensionalidad y la precisión del sistema; y asimismo presentamos un algoritmo no lineal conveniente para dispositivos con limitaciones de hardware que solo utiliza aritmética de punto fijo en la fase de predicción y que permite reducir la complejidad del modelo de aprendizaje mientras mantiene el rendimiento del sistema. La eficacia del sistema propuesto es verificada a través de una experimentación extensiva sobre la base de datos RAH que hemos generado y hecho pública en la red. Esta contiene la información inercial obtenida de un grupo de 30 participantes que realizaron una serie de actividades de la vida cotidiana en un ambiente controlado mientras tenían sujeto a su cintura un smartphone que capturaba su movimiento. Los resultados obtenidos en esta investigación demuestran que es posible realizar el RAH en tiempo real con una precisión cercana al 97%. De esta manera, podemos emplear la metodología propuesta en aplicaciones de alto nivel que requieran el RAH tales como monitorizaciones ambulatorias para personas dependientes (ej. ancianos o discapacitados) durante periodos mayores a cinco días sin la necesidad de recarga de baterías.

3

Outten, Alan Gerard. "Analysis of human muscle activity." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/7958.

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4

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 this project we develop an algorithm to know the activity of a person using accelerometer available in Smartphone. We have extracted the Smartphone accelerometer data using an application called accelerometer data logger version 1.0 available in Smartphone market and have processed the data in Matlab for classifying the different activities of human being into static and dynamic activity, if the activity is dynamic then further classification into walking or running is performed with the algorithm. We implemented smoothening filters for data analysis and statistical techniques like standard deviation, mean and signal magnitude analysis for activity classification. This classification algorithm will let us know the type of activity either static or dynamic and then classify the position of the user, such as walking, running or ideal, which can provide useful information for the observer who is monitoring the activities of wearer, and which will help the wearer for his daily living. To bring out the extensive use of algorithm and to provide valuable feedback for wearer regarding his activities, energy spent by user during the activities was calculated at a given time using regression methods and was implemented in the algorithm. The developed model was able to estimate the energy spent by the user, the observations recorded were almost similar to the treadmill data which is taken as a standard for our model and the mean error is not more than ±2 for 30 observations. The final results when compared with the standard model was proved to be 93 % accurate on average of 30 subjects data which is used for verifying the algorithm developed. With these set of results we have come to a conclusion that algorithm can be easily implemented in a real time Smartphone application with low false predictions and can be implemented with low computational cost and fast real-time response. In future our classification algorithm can also be used in military applications where one can know what the soldier is doing without actually seeing him and additionally it can be proved as a support system in athlete’s health monitoring and training.
I denna modell har vi utvecklat en algoritm för aktivitetsklassificeringoch energiförbrukning uppskattning , vilket hjälper oss i övervakningen daglig mänsklig aktivitet med större noggrannhet . Resultaten valideras med standard energiförbrukning teknik och aktivitetsklassificeringsvideoobservationer. Vi vill att denna modell ska integreras i smarta mobiltelefoner för att ge slutanvändaren en vänlig atmosfär utan att lägga några komplicerade funktioner för hantering av utrustningen . Denna modell är mycket användbart i klinisk uppföljning av patienterna , kommer det att hjälpa oss att övervaka gamla , sjuka och utvecklingsstörda personens aktivitetsidentifiering och hjälper oss i nära övervakning av patienterna men fysiskt att vara borta från dem . Våra bärbara MEMS baserade treaxlig accelerometer system baserat smartphone kompatibel algoritm tillsammans med andra fysiologiska övervakningsparametrarkommer att ge korrekt övervakning rörelse och energiförbrukning uppskattning för klinisk analys . Denna modell är användbar för analys och övervakning av grupp -och enskilda individer , vilket kommer att leda till att spåra deras rörelser och en framgångsrik räddningsaktion för att rädda dem från dödliga sjukdomar och förebygga risker när de är skadade . Framtida arbete kommer att vara kontinuerlig övervakning av ämnen enskild aktivitet tillsammans med gruppaktivitet . Identifiera hållning övergång av olika aktiviteter i en kort tid som att springa till sittande , sittande till stående , står att krypa etc.
0091-7660885577

5

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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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 software. Afterwards, the software design was done in two phases. In the first phase, we focused on four simple activities to be recognized by the wearable device. We used Weka machine learning tool to build a mathematical model which could recognize the four activities with the accuracy of 99.32%. Moreover, we proposed an algorithm to measure the intensity of the activities with the accuracy of 93%. In the second phase, we focused on eight complex warm-up exercises. After building the mathematical model, the WHAR system could recognize the eight activities with the accuracy of 95.60%.

6

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 represented as a composition of temporal texture primitives with its salient spatio-temporal features, which are also the symbols of our codebook. To overcome temporal variation between different performances of the same action, a Profile Hidden Markov Model is applied with Viterbi Path Counting (ensemble training). Not only parameters and structure but also codebook is learned during training.

7

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 activity description proposed by Ryoo et al. (2009) is implemented and extended. The feature histogram is widely used in feature based approaches
however, the feature relation histogram has the ability to represent the locational information of the features. Our extension defines a new set of relations between the features, which makes the method more effective for action description. Classifications are performed and results are compared using feature histogram, Ryoo&rsquo
s feature relation histogram and our feature relation histogram using the same datasets and the feature type. Our experiments show that feature relation histogram performs slightly better than the feature histogram, our feature relation histogram is even better than both of the two. Although the difference is not clearly observable in the datasets containing periodic actions, a 12% improvement is observed for the non-periodic action datasets. Our work shows that the spatio-temporal relation represented by our new set of relations is a better way to represent the activity for classification.

8

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-of-the-art perception techniques. The environment that the robot patrols is assumed to be an indoor environment such as an office. We show how we can generate HARs in correct regions by clustering human position data. The experimental evaluations show that we can do so in different indoor environments, with data acquired from different sensors and that the system can handle noise.
Mänskliga aktivitetsregioner, HARs (Human Activity Regions) är människocentreraderegioner som ger en semantisk partitionering av inomhusmiljöer. HARs är användbara för att uppnå väl fungerande människarobot- interaktioner. I denna avhandling utformas ett system för att generera HARs automatiskt baserat på data från robotar. Detta görs genom att klustra observationer av människor för att på så vis få fram de områden som är associerade med frekvent mänsklig närvaro. Experiment visar att systemet kan hantera data som registrerats av olika sensorer i olika inomhusmiljöer och att det är robust. Framförallt genererar systemet en pålitlig partitionering av miljön.

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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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10

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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Книги з теми "Human activity":

1

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

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2

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

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3

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

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4

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

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5

Naur, Peter. Computing, a human activity. New York: ACM Press, 1992.

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6

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

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7

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

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8

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

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Анотація:

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. The influence of the PCF on the vital activity of the state and man is systematically described: mentality, systemic thinking, human health, ensuring the safety of life, sustainability of development, agricultural production, housing and communal services, construction, industry, information security, parrying of the PCF, the influence of the PCF on the development of science and education. Climate change trends at the global and regional levels are also described. Estimates of the impact of the PCF on the economy of the state and regions, recommendations on the adaptation of the economy to the PCF, the relationship of information security and information about the PCF, information technologies for assessing the sustainability of development and investment attractiveness of territories, conceptual foundations of state anti-crisis management of socio-economic systems are presented. It is intended for researchers, teachers, postgraduates, students specializing in the field of life safety, computer ecological and economic monitoring. It can be used to educate society in the field of the natural environment and its natural and climatic conditions.

9

Hu, Zhongxu, and Chen Lv. Vision-Based Human Activity Recognition. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2290-9.

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10

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

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Частини книг з теми "Human activity":

1

Welle, Stephen. "Physical Activity." In Human Protein Metabolism, 177–95. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1458-8_8.

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2

Komukai, Kohei, and Ren Ohmura. "Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording." In Human Activity Sensing, 3–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_1.

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3

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, 135–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_10.

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4

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, 153–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_11.

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5

Osmani, Aomar, and Massinissa Hamidi. "Bayesian Optimization of Neural Architectures for Human Activity Recognition." In Human Activity Sensing, 171–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_12.

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6

Widhalm, Peter, Maximilian Leodolter, and Norbert Brändle. "Into the Wild—Avoiding Pitfalls in the Evaluation of Travel Activity Classifiers." In Human Activity Sensing, 197–211. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_13.

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7

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, 213–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_14.

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8

Janko, Vito, Martin Gjoreski, Gašper Slapničar, Miha Mlakar, Nina Reščič, Jani Bizjak, Vid Drobnič, et al. "Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge." In Human Activity Sensing, 233–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_15.

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9

Scholl, Philipp M., and Kristof Van Laerhoven. "Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data." In Human Activity Sensing, 17–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_2.

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10

Nozaki, Junto, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. "Compensation Scheme for PDR Using Component-Wise Error Models." In Human Activity Sensing, 29–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13001-5_3.

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Тези доповідей конференцій з теми "Human activity":

1

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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2

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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3

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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4

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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5

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

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6

Nair, Nitin, Chinchu Thomas, and Dinesh Babu Jayagopi. "Human Activity Recognition Using Temporal Convolutional Network." In iWOAR '18: 5th international Workshop on Sensor-based Activity Recognition and Interaction. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3266157.3266221.

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7

"Ayllu: Agent-Inspired Cooperative Services for Human Interaction." In The 3rd International Workshop on Computer Supported Activity Coordination. SciTePress - Science and and Technology Publications, 2006. http://dx.doi.org/10.5220/0002480600550064.

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8

Zhdanova, Marina, Viacheslav V. Voronin, Evgeny Semenishchev, Yurii V. Ilyukhin, and Aleksandr Zelensky. "Human activity recognition for efficient human-robot collaboration." In Artificial Intelligence and Machine Learning in Defense Applications II, edited by Judith Dijk. SPIE, 2020. http://dx.doi.org/10.1117/12.2574133.

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9

Cheng, Zhiqing, Stephen Mosher, Huaining Cheng, and Timothy Webb. "Human activity recognition based on human shape dynamics." In SPIE Defense, Security, and Sensing, edited by Ivan Kadar. SPIE, 2013. http://dx.doi.org/10.1117/12.2015487.

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10

Saroja, M. N., K. R. Baskaran, and P. Priyanka. "Human pose estimation approaches for human activity recognition." In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2021. http://dx.doi.org/10.1109/icaeca52838.2021.9675787.

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Звіти організацій з теми "Human activity":

1

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

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2

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

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3

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

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4

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

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5

Camp, John, Darrell Lochtefeld, Zhiqing Cheng, Isiah Davenport, Tim MtCastle, Steve Mosher, Jeanne Smith, and Max Grattan. Biofidelic Human Activity Modeling and Simulation with Large Variability. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada618197.

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6

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), January 2017. http://dx.doi.org/10.2172/1343540.

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7

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

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8

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, May 2018. http://dx.doi.org/10.7546/crabs.2018.04.08.

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9

Matzen, Laura E., Michael Joseph Haass, Michael Christopher Stefan Trumbo, Austin Ray Silva, Susan Marie Stevens-Adams, Jennifer Taylor White, Anna Ho, and David Eugene Peercy. Using recordings of brain activity to predict and improve human performance. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1055638.

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10

Manfredi, James. Regulation of the Tumor Suppressor Activity of p53 in Human Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada395583.

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