A human behavior modeling system /
نظام نمذجة السلوك انساني
Moataz Maher Kilany ; Supervised Aboul Ella Hassanien , Amr Ahmed Badr , Ammar Yasser Eladl
- Cairo : Moataz Maher Kilany Abdelaziz , 2018
- 66 Leaves : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science
Computer systems are major sources of problem solving in our daily life. The revolution of sensory technology and human-machine interfaces greatly affected research in how machine could detect and act properly against human actions. One of the most effec-tive approaches for human behavior analysis, simulation and understanding is modeling techniques. Researchers have introduced many approaches for modeling typical classes of behavior. Human behavior prediction and classication is also a core problem to be solved in any human behavior analysis model. Classication and prediction still suffer from a number of bottlenecks for selecting the best classication model that gives best classication results for a given data source. Fine-tuning classication parameters for a classier to make best classication results in terms of generalization and classication accuracy is a known problem in research and is dependent on the data source being ex-perimented. In this work a set of modeling concepts were applied to design a behavior analysis model in terms of a set of data models and process models that incorporate together to build a system that should be able to qualify, quantify and analyze human behaviors. We also used biosignals as sources for human behavior learning and analysis in terms of electromyography (EMG) signals sensored over human skin and VICON data acquired from motion capturing systems. This research introduced a classication approaches that employed swarm intelligence for selecting best classication models and selecting most releveant features from a set of data sources