Zeitschriftenartikel: „Human activity“

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Patel, Mayur A. „Combating Human Diseases through Physical Activity“. Indian Journal of Applied Research 3, Nr. 2 (01.10.2011): 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 und Vikashini S. „Human Activity Recognition Using Smartphone Sensors“. Webology 18, Nr. 04 (28.09.2021): 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.

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Zhang, Tongda, Xiao Sun, Yueting Chai und Hamid Aghajan. „Human Computer Interaction Activity Based User Identification“. International Journal of Machine Learning and Computing 4, Nr. 4 (2014): 354–58. http://dx.doi.org/10.7763/ijmlc.2014.v4.436.

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P. Ambiga, P. Ambiga, R. Bhavani R. Bhavani, P. Sivamani P. Sivamani und R. R. Thanighai arassu. „Comparative Analysis of Microbial and Human Amylase Activity“. Indian Journal of Applied Research 3, Nr. 3 (01.10.2011): 380–84. http://dx.doi.org/10.15373/2249555x/mar2013/130.

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Guda, B. B., V. V. Pushkarev, O. V. Zhuravel, A. Ye Kovalenko, V. M. Pushkarev, Y. M. Taraschenko und M. D. Tronko. „Protein kinase Akt activity in human thyroid tumors“. Ukrainian Biochemical Journal 88, Nr. 5 (31.10.2016): 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, Nr. 3 (Juni 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>

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Khupavtseva, Nataliia, und Liana Onufriieva. „Facilitative Interaction as a Multi-Level Human Activity“. Collection of Research Papers "Problems of Modern Psychology" 59 (30.03.2023): 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.

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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 und Bo-Jue Wang Tao Zhang. „Human Activity Recognition with Multimodal Sensing of Wearable Sensors“. 電腦學刊 32, Nr. 6 (Dezember 2021): 024–37. http://dx.doi.org/10.53106/199115992021123206003.

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Bobrovnik, S. A., M. A. Demchenko und S. V. Komisarenko. „Age changes of human serum polyreactive immunoglobulins (PRIG) activity“. Ukrainian Biochemical Journal 86, Nr. 5 (27.10.2014): 151–55. http://dx.doi.org/10.15407/ubj86.05.151.

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Dönmez, İlknur. „Human Activity Analysis and Prediction Using Google n-Grams“. International Journal of Future Computer and Communication 7, Nr. 2 (Juni 2018): 32–36. http://dx.doi.org/10.18178/ijfcc.2018.7.2.516.

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KUZMINSKA, Natalia, und Olga KOZHEMIACHENKO. „IMPACT OF NATIONAL HUMAN CAPITAL ON POPULATION MIGRATION ACTIVITY“. Herald of Khmelnytskyi National University. Economic sciences 312, Nr. 6(2) (29.12.2022): 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 capital (implementation of high-quality economic production; quality of life). Based on the collected data, a cluster analysis was carried out. As a result of the study, it was concluded that the factors that characterize the conditions for receiving income from human capital have the greatest influence on migration. The research was carried out until February 24, 2022. Therefore, they can become the basis for studying the migration activity of the population in the new military-economic and political realities.

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Maradani, Lohitha. „Human Activity Recognition“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 7 (31.07.2022): 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. Typically, HAR system can be either supervised or unsupervised. A supervised HAR system required some prior training with dedicated datasets while unsupervised HAR system is being configured with a set of rules during development. HAR is considered as an important component in various scientific research contexts i.e. surveillance, healthcare and human computer interaction.

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Kaewunruen, Sakdirat, Jessada Sresakoolchai, Junhui Huang, Satoru Harada und Wisinee Wisetjindawat. „Human Activity Vibrations“. Data 6, Nr. 10 (30.09.2021): 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 AI model in a smartphone which has the potential to collect people’s vibration data and decide what movement is being conducted. Moreover, with more data received, the database can be updated and used to train the model with a larger dataset. The prevalence of the smartphone opens the door to crowdsensing, which leads to the pattern of people taking public transport being understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transport, services and schedules can be planned perceptively.

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Aggarwal, J. K., und M. S. Ryoo. „Human activity analysis“. ACM Computing Surveys 43, Nr. 3 (April 2011): 1–43. http://dx.doi.org/10.1145/1922649.1922653.

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Ball, Philip. „Predicting human activity“. Nature 465, Nr. 7299 (Juni 2010): 692. http://dx.doi.org/10.1038/465692a.

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Solomon, S., N. Muruganantham und M. M. Senthamilselvi. „ANTICANCER ACTIVITY OF ABELMOSCHUS ESCULENTUS (FLOWERS) AGAINST HUMAN LIVER CANCER“. International Journal of Pharmacy and Biological Sciences 6, Nr. 3 (01.07.2016): 154–57. http://dx.doi.org/10.21276/ijpbs.2016.6.3.18.

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Lateef, Rana Abdulrahman, und Dr Ayad Rodhan Abbas. „A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers“. Webology 19, Nr. 1 (20.01.2022): 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 Gradient Boosting (ConvXGBoost), which combines Convolutional Neural Network (CNN) represented by AlexNet to learn the input features automatically, followed by XGBoost decision tree used to predict the class label and thereof recognize the performed activity. Human activities are collected from sensors as time series data. Therefore, we suggested using one-dimensional AlexNet (1D AlexNet) model instead of 2D. The AlexNet model is compiled with two optimizers Adam and Stochastic Gradient Descent (SGD) which are applied consecutively. The suggested architecture was trained and evaluated on the “WISDM Smartphone and Smartwatch Activity and Biometric Dataset” that consists of raw data for eighteen activities recorded from phone and watch. The experiments revealed that using multi optimizer with a convolutional neural network improved the accuracy of recognition by 5%. Moreover, a proposed ConvXGBoost model outperformed the performance of other models works with the dataset as mentioned above with an overall accuracy of 98-99% depends on the device used.

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Lutsenko, T. N., M. V. Kovalenko und O. Yu Galkin. „Validation of biological activity testing procedure of recombinant human interleukin-7“. Ukrainian Biochemical Journal 89, Nr. 1 (21.02.2017): 82–89. http://dx.doi.org/10.15407/ubj89.01.082.

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

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Rai, R. K., Michael Balmer, Marcel Rieser, V. S. Vaze, Stefan Schönfelder und Kay W. Axhausen. „Capturing Human Activity Spaces“. Transportation Research Record: Journal of the Transportation Research Board 2021, Nr. 1 (Januar 2007): 70–80. http://dx.doi.org/10.3141/2021-09.

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Shukla, Harsh, und Meenu Pandey. „Human Suspicious Activity Recognition“. International Innovative Research Journal of Engineering and Technology 5, Nr. 4 (30.06.2020): 14–17. http://dx.doi.org/10.32595/iirjet.org/v5i4.2020.130.

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Bhambri, Pankaj, Harpreet Kaur, Akarshit Gupta und Jaskaran Singh. „Human Activity Recognition System“. Oriental journal of computer science and technology 13, Nr. 0203 (30.01.2021): 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 various applications like surveillance, aiding purpose etc. This system will not only be cost effective but also as a utility-based system that can be incorporated in a large number of applications that will save time and aid in various activities that require recognition process, and save a lot of time with good accuracy Also, it will aid the blind people in availing the knowledge of their surroundings.

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Guimarães, C. P., G. L. Cid, V. S. Santos, M. C. P. Zamberlan, F. C. H. Pastura, G. M. D. Abud, C. Lessa, D. S. Batista und M. M. Fraga. „Human activity reference database“. Work 41 (2012): 613–16. http://dx.doi.org/10.3233/wor-2012-0218-613.

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Thomas, E. L., R. I. Lehrer und R. F. Rest. „Human Neutrophil Antimicrobial Activity“. Clinical Infectious Diseases 10, Supplement 2 (01.08.1988): S450—S456. http://dx.doi.org/10.1093/cid/10.supplement_2.s450.

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Aloimonos, Y. „HAL: Human Activity Language“. Journal of Vision 8, Nr. 6 (19.03.2010): 1050. http://dx.doi.org/10.1167/8.6.1050.

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Wojtyla, Aneta, Marta Gladych und Blazej Rubis. „Human telomerase activity regulation“. Molecular Biology Reports 38, Nr. 5 (18.11.2010): 3339–49. http://dx.doi.org/10.1007/s11033-010-0439-x.

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Giri, Pranit. „Human Activity Recognition System“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 5 (31.05.2023): 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 Computer Science and Information Technology (FCSIT). The proposed system offers a predictive system that can predict the student's performance in the course "TMC1013 System Analysis and Design", which in turn assists the lecturers from the Information System department to identify students that are predicted to have bad performance in the course "TMC1013 System Analysis and Design". The proposed system offers student performance prediction through the rules generated via the data mining technique. The data mining technique used in this project is classification, which classifies the students based on students' grades. Keywords- Student performance; student analysis; data mining; student performance analysis; classification; prediction; system

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Wong, James. „Human All too Human: Gossip as Activity“. International Journal of the Humanities: Annual Review 2, Nr. 1 (2006): 0. http://dx.doi.org/10.18848/1447-9508/cgp/v02i01/42878.

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Chung, Ha Sook. „Antiproliferative Activity of Allium monanthum in HT-29 Human Colorectal Adenocarcinoma Cells“. Korean Tea Society 28, Nr. 3 (30.09.2022): 22–32. http://dx.doi.org/10.29225/jkts.2022.28.3.22.

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This study was designed to identify the phytochemicals of Allium monanthum with antiproliferative effect on HT-29 human colorectal adenocarcinoma cells through the activity-guided fractionation and isolation method. In particular, compounds 1-8 were isolated from A. monanthum, and determined to be apigenin (1), isorhamnetin (2), 5,7,3′,4′-tetrahydroxy-3-methoxyflavone (3), luteolin (4), myricetin (5), isorhamnetin-3-O-β-D-glucoside (6), isorhamnetin-3-O-β-D-rutinoside (7), and luteolin-7-O-β-D-glucoside (8) by comparing the 1D-, 2D-NMR, and ESI-MS spectral data. Of these, compound 5 significantly decreased the cell viability (IC50, 48.21 ± 1.07 μM) with a morphological change, indicative of apoptosis, resulting in activation of caspase-9, caspase-3, and poly-ADP-ribose polymerase in a dose-dependent manner.

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Suriani, Nor Surayahani, und Fadilla ‘Atyka Nor Rashid. „Smartphone Sensor Accelerometer Data for Human Activity Recognition Using Spiking Neural Network“. International Journal of Machine Learning and Computing 11, Nr. 4 (August 2021): 298–303. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1051.

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Recognizing human actions is a challenging task and actively research in computer vision community. The task of human activity recognition has been widely used in various application such as human monitoring in a hospital or public spaces. This work applied open dataset of smartphones accelerometer data for various type of activities. The analogue input data is encoded into the spike trains using some form of a rate-based method. Spiking neural network is a simplified form of dynamic artificial network. Therefore, this network is expected to model and generate action potential from the leaky integrate-and-fire spike response model. The leaning rule is adaptive and efficient to present synapse exciting and inhibiting firing neuron. The result found that the proposed model presents the state-of-the-art performance at a low computational cost.

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KOYAMA, Hikaru, Miki HAYAKAWA und Norihiko MORIWAKI. „Human Big Data Sensor for Human Activity Measurement“. Journal of the Society of Mechanical Engineers 117, Nr. 1150 (2014): 614–15. http://dx.doi.org/10.1299/jsmemag.117.1150_614.

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Dogan, Gulustan. „Advances in Human Activity Recognition“. Computer 54, Nr. 5 (Mai 2021): 4–6. http://dx.doi.org/10.1109/mc.2021.3055671.

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Kulminskaya, Anna A., Andrew N. Saveliev und Kirill N. Neustroev. „Human Abzymes with Amylolytic Activity“. Trends in Glycoscience and Glycotechnology 16, Nr. 87 (2004): 17–31. http://dx.doi.org/10.4052/tigg.16.17.

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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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Jensen, H. S. „Elastolytic Activity of Human Monocytes“. Scandinavian Journal of Rheumatology 22, Nr. 1 (Januar 1993): 48. http://dx.doi.org/10.3109/03009749309095113.

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Van der Spek, Stefan, Jeroen Van Schaick, Peter De Bois und Remco De Haan. „Sensing Human Activity: GPS Tracking“. Sensors 9, Nr. 4 (24.04.2009): 3033–55. http://dx.doi.org/10.3390/s90403033.

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atheed, mais irreem, Dena Ahmed und Rashad Kamal. „Human Activity Recognition: literature Review“. JOURNAL OF EDUCATION AND SCIENCE 30, Nr. 5 (01.12.2021): 12–29. http://dx.doi.org/10.33899/edusj.2021.130293.1162.

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Sevinç, Tuğba. „The Nature of Human Activity“. Kilikya Felsefe Dergisi / Cilicia Journal of Philosophy 6, Nr. 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 elements. By way of correcting Arendt’s misreading of Marx, my purpose has been to get a better understanding of the theories of Marx and Arendt, as well as to see their incompatible views regarding the nature of human activity and of freedom. Arendt charges Marx of ignoring the most central human activity, that is ‘action’; and of denying human beings a genuine political existence and freedom. Furthermore, according to Arendt, Marx conceives labor as human being’s highest activity and ignores the significance of other two activities, namely work and action. In the last analysis, Marx and Arendt prioritizes distinct human activities as the most central (labor and action, respectively) to human beings; and as a result, they provide us two irreconcilable views of politics, history and freedom.

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Roy, Debaditya, Sarunas Girdzijauskas und Serghei Socolovschi. „Confidence-Calibrated Human Activity Recognition“. Sensors 21, Nr. 19 (30.09.2021): 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 produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.

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Jadhav Sarnaik, Neha Nilesh. „Human Activity Recognition using CNN“. International Journal of Scientific and Research Publications (IJSRP) 10, Nr. 2 (06.02.2020): p9804. http://dx.doi.org/10.29322/ijsrp.10.02.2020.p9804.

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Xiang, Jianwen, Jing Tian und Akira Mori. „Goal-directed human activity computing“. Journal of Ambient Intelligence and Smart Environments 3, Nr. 2 (2011): 127–45. http://dx.doi.org/10.3233/ais-2011-0103.

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Shay, Jerry W., und Woodring E. Wright. „Telomerase activity in human cancer“. Current Opinion in ONCOLOGY 8, Nr. 1 (Januar 1996): 66–71. http://dx.doi.org/10.1097/00001622-199601000-00012.

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Le, Siyuan, Jay Jiguang Zhu, Douglas C. Anthony, Carol W. Greider und Peter McL Black. „Telomerase Activity in Human Gliomas“. Neurosurgery 42, Nr. 5 (01.05.1998): 1120–24. http://dx.doi.org/10.1097/00006123-199805000-00099.

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Raffel, Corey. „Telomerase Activity in Human Gliomas“. Neurosurgery 42, Nr. 5 (01.05.1998): 1124–25. http://dx.doi.org/10.1097/00006123-199805000-00100.

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Piepmeier, Joseph M. „Telomerase Activity in Human Gliomas“. Neurosurgery 42, Nr. 5 (01.05.1998): 1125. http://dx.doi.org/10.1097/00006123-199805000-00101.

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Rutka, James T. „Telomerase Activity in Human Gliomas“. Neurosurgery 42, Nr. 5 (01.05.1998): 1125. http://dx.doi.org/10.1097/00006123-199805000-00102.

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Tryon, Warren W. „Methods of measuring human activity.“ Journal of Behavior Analysis in Health, Sports, Fitness and Medicine 1, Nr. 1 (2008): 58–71. http://dx.doi.org/10.1037/h0100369.

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Park, Mi Seul, Hong-Duc Phan, Florian Busch, Samantha H. Hinckley, James A. Brackbill, Vicki H. Wysocki und Kotaro Nakanishi. „Human Argonaute3 has slicer activity“. Nucleic Acids Research 45, Nr. 20 (11.10.2017): 11867–77. http://dx.doi.org/10.1093/nar/gkx916.

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Shen, Yijun, Longzhi Yang, Edmond S. L. Ho und Hubert P. H. Shum. „Interaction-Based Human Activity Comparison“. IEEE Transactions on Visualization and Computer Graphics 26, Nr. 8 (01.08.2020): 2620–33. http://dx.doi.org/10.1109/tvcg.2019.2893247.

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Singer, R., H. Levinsky, M. Sagiv, M. Barnet, C. Servadio und D. Allalouf. „Deoxyribonuclease Activity in Human Sperm“. Archives of Andrology 15, Nr. 2-3 (Januar 1985): 105–7. http://dx.doi.org/10.3109/01485018508986898.

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