Tesis sobre el tema "Human Activity Prediction"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 20 mejores tesis para su investigación sobre el tema "Human Activity Prediction".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore tesis sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Coen, Paul Dixon. "Human Activity Recognition and Prediction using RGBD Data". OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2562.
Texto completoBergelin, Victor. "Human Activity Recognition and Behavioral Prediction using Wearable Sensors and Deep Learning". Thesis, Linköpings universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138064.
Texto completoBaldo, Fatima Magdi Hamza. "Integrating chemical, biological and phylogenetic spaces of African natural products to understand their therapeutic activity". Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289714.
Texto completoSnyder, Kristian. "Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.
Texto completoMehdi, Nima. "Approches probabilistes pour la perception et l’interprétation de l’activité humaine". Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0202.
Texto completoFrom industry to services, intelligent systems are required to observe, interact with, or cooperate with humans. This thesis is therefore set in the context of intelligent perception methods for the analysis of humans, using the pose and activity associated with them. Due to the variable and changing nature of humans, it is difficult to obtain an accurate representation of theprocesses guiding their movements and actions. These difficulties are compounded when it comes to estimating or predicting movements or activities. In order to take account of the uncertainty inherent in humans, we propose a Bayesian approach to the perception and analysis of human activity. The first contribution is dedicated to the simultaneous estimation of human pose and posture. Using a monocular camera and wearable sensors, we aim to estimate human 3D pose in real time. For robust estimation, a multimodal fusion approach is suggested, incorporating measurements from wearable inertial sensors with camera observations. In this way, we overcome measurement ambiguities related to the camera and inertial drift due to inertial units. We use a particle filter so as to take into account the non-deterministic nature of human motion and thenon-Gaussian nature of posture. In order to reduce the computational cost, we put forward an architecture composed of two consecutive filters. A first filter estimates the posture in a factorized way from inertial observations only. Then a second filter estimates the complete pose from the camera, incorporating the estimation of the first filter. Our approach achieves fusion by constructing the sampling distribution of the second filter. This architecture makes it possible to estimate pose and posture simultaneously, at low computational cost, and is robust to cloaking and drift. The second contribution pertains to the prediction of human activity. Hidden Markov models have proved effective for the analysis of human activity through segmentation and activity recognition tasks. However, they have modeling limitations that make them insufficient for prediction. We therefore propose the use of semi-Markovian models for prediction. These models extend the definition of Markov models by explicitly modeling the duration spent in each state. This explicit modeling of duration enables better modeling of non-stationary processes and improves the predictive capability of these models. Our study thus demonstrates the usefulness of such models for activity prediction while taking uncertainty into account
Rozman, Peter Andrew. "Multi-Unit Activity in the Human Cortex as a Predictor of Seizure Onset". Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:15821597.
Texto completoKarst, Gregory Mark. "Multijoint arm movements: Predictions and observations regarding initial muscle activity at the shoulder and elbow". Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184920.
Texto completoCheradame, Stéphane. "Biomodulation du 5-fluorouracile par l'acide folinique et recherche des facteurs de prédiction de la sensibilité tumorale à cette association". Université Joseph Fourier (Grenoble ; 1971-2015), 1996. http://www.theses.fr/1996GRE10252.
Texto completoSilva, Joana. "Smartphone Based Human Activity Prediction". Dissertação, 2013. http://hdl.handle.net/10216/74272.
Texto completoSilva, Joana Raquel Cerqueira da. "Smartphone based human activity prediction". Master's thesis, 2013. http://hdl.handle.net/10216/72620.
Texto completoSilva, Joana Raquel Cerqueira da. "Smartphone Based Human Activity Prediction". Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/67649.
Texto completoSilva, Joana Raquel Cerqueira da. "Smartphone based human activity prediction". Dissertação, 2013. http://hdl.handle.net/10216/72620.
Texto completoJardim, David Walter Figueira. "Human activity recognition and prediction in RGB-D videos". Doctoral thesis, 2018. http://hdl.handle.net/10071/19571.
Texto completoHuman Activity Recognition is an interdisciplinary research area that has been attracting interest from several research communities specialized in machine learning, computer vision, and medical research. The potential applications range from surveillance systems, human computer interfaces, sports analysis, digital assistants, collaborative robots, health-care and self-driving cars. Capturing human activity presents technical difficulties like occlusion, insufficient lighting, unreliable tracking and ethical concerns. Human motion can be ambiguous and have multiple intents. The complexity of our lives and how we interact with other humans and objects prompt to a nearly infinite combination of variations in how we do things. The focus of this dissertation is to develop a system capable of recognizing and predicting human activity using machine learning techniques to extract meaning from features computed from relevant joints of the human body captured by the skeleton tracker of the Kinect sensor. We propose a modular framework that performs off-line temporal segmentation of sequences of actions, off-line semi unsupervised labeling of sub-activities via clustering techniques, real-time frame by-frame sub-activity recognition using random decision forest binary classifiers right from the very first frames of the action and real-time activity prediction with conditional random fields to model the sequential structure of sequences of actions to reason about future possibilities. We recorded a new dataset containing long sequences of aggressive actions with a total of 72 sequences, 360 samples of 8 distinct actions performed by 12 subjects. We experimented extensively with two different datasets, compared the recognition performance of several supervised classifiers trained with manually labeled data versus semi-unsupervised labeled data. We learned how the quality of the training data affects the results which also depends on the complexity of the actions being recognized. We outperformed state-ofthe-art activity recognition approaches, performed early action recognition and obtained encouraging results in activity prediction.
"Real Time Estimation and Prediction of Similarity in Human Activity Using Factor Oracle Algorithm". Master's thesis, 2016. http://hdl.handle.net/2286/R.I.38796.
Texto completoDissertation/Thesis
Masters Thesis Electrical Engineering 2016
(7043231), Xiaoyu Yu. "Human Activity Recognition Using Wearable Inertia Sensor Data adnd Machine Learning". Thesis, 2019.
Buscar texto completoChen, Hong-Bin y 陳弘彬. "Prediction of Human Multidrug Transporter P-Glycoprotein Inhibition Activity Using Pharmacophore Ensemble/Support Vector Machine Approach". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/46985220745933953368.
Texto completo國立東華大學
化學系
98
P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the overexpression of P-glycoprotein (P-gp) by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop P-gp inhibitors that can reverse MDR in the process of drug discovery and development. An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 31, r2 = 0.89, q2 = 0.86, RMSE=0.40, s = 0.28), the test set (n = 88, r2 = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 10, r2 = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. Thus, it can be asserted that this PhE/SVM model is an accurate, fast and robust model and can be employed to predict P-gp inhibitor binding affinity to facilitate drug discovery and drug development by designing drug candidates with better metabolism profile.
Ziaeetabar, Fatemeh. "Spatio-temporal reasoning for semantic scene understanding and its application in recognition and prediction of manipulation actions in image sequences". Thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0005-1381-3.
Texto completoKonvalinka, Ana. "Angiotensin II Proteomic Signature in Human Proximal Tubular Cells as a Predictor of Renin Angiotensin System Activity in Kidney Diseases". Thesis, 2014. http://hdl.handle.net/1807/65676.
Texto completo"A System Identification and Control Engineering Approach for Optimizing mHealth Behavioral Interventions Based on Social Cognitive Theory". Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.40275.
Texto completoDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2016
McCanna, David. "Development of Sensitive In Vitro Assays to Assess the Ocular Toxicity Potential of Chemicals and Ophthalmic Products". Thesis, 2009. http://hdl.handle.net/10012/4338.
Texto completo