Good
Prof. Joseph is one of the best engineering profs at Berks. You can see that he really wants you to learn the basic concepts and be creative with more complex solutions. He gets a bad rep for pushing students to think, but it really helps us learn the material better. He's an amazing prof!
Pennsylvania State University Berks - Engineering
Associate Professor at Penn State Berks
Mechanical or Industrial Engineering
Joseph
Mahoney
Allentown, Pennsylvania
Research in Human Motion, Low Cost Biomechanic Measurement Devices, Nonlinear Dynamics, Virtual Reality, Video Game Design
Research Assistant
Joseph worked at Penn State University as a Research Assistant
Lecturer
Lecturer in Mathematics
Assistant Professor
Joseph worked at Penn State Berks as a Assistant Professor
Associate Professor
Joseph worked at Penn State Berks as a Associate Professor
Faculty Lecturer
Teaching Statics, System Dynamics and Material Mechanics
Instructor
Instructor of "Principles of Engineering Design"
Summers
Bachelor of Science (B.S.)
Mechanical Engineering
Schreyer Honors College
Minors in both Mathematics and Engineering Mechanics
Master of Science (M.S.)
Mechanical Engineering
Thesis: A Bimetallic Valve Solution for a Hydrogen-Powered Micro-Generator
Doctor of Philosophy (Ph.D.)
Engineering Science and Mechanics
Research Assistant
Lecturer
Lecturer in Mathematics
Penn State University
Penn State University
Penn State University
Penn State University
Penn State University
Penn State University
Applied Ergonomics
Penn State University
Applied Ergonomics
Springer New York
Using the concept of task manifolds, a number of data analysis methods have been used to explain how redundancy influences the structure of variability observed during repeated motor performance. Here we describe investigations that integrate the task manifold perspective with the analysis of Inter-Trial task dynamics. Goal equivalent manifolds (GEMs), together with optimal control ideas, are used to formulate simple models that serve as experimentally testable hypotheses on how Inter-Trial fluctuations are generated and regulated. In an experimental context, these phenomenological models allow us to show how error-correcting control is spatiotemporally organized around a given GEM. To illustrate our approach, we apply it to study the variability observed in a virtual shuffleboard task. The geometric stability properties of the Inter-Trial dynamics near the GEM are extracted from fluctuation time series data. We find that subjects exhibit strong control of fluctuations in an eigendirection transverse to the GEM, whereas they only weakly control fluctuations in an eigendirection nearly, but not exactly, tangent to it. We demonstrate that our dynamical analysis is robust under coordinate transformations, and discuss how our results support a generalized interpretation of the minimum intervention principle that suggests the involvement of competing costs in addition to goal-level error minimization.
Penn State University
Applied Ergonomics
Springer New York
Using the concept of task manifolds, a number of data analysis methods have been used to explain how redundancy influences the structure of variability observed during repeated motor performance. Here we describe investigations that integrate the task manifold perspective with the analysis of Inter-Trial task dynamics. Goal equivalent manifolds (GEMs), together with optimal control ideas, are used to formulate simple models that serve as experimentally testable hypotheses on how Inter-Trial fluctuations are generated and regulated. In an experimental context, these phenomenological models allow us to show how error-correcting control is spatiotemporally organized around a given GEM. To illustrate our approach, we apply it to study the variability observed in a virtual shuffleboard task. The geometric stability properties of the Inter-Trial dynamics near the GEM are extracted from fluctuation time series data. We find that subjects exhibit strong control of fluctuations in an eigendirection transverse to the GEM, whereas they only weakly control fluctuations in an eigendirection nearly, but not exactly, tangent to it. We demonstrate that our dynamical analysis is robust under coordinate transformations, and discuss how our results support a generalized interpretation of the minimum intervention principle that suggests the involvement of competing costs in addition to goal-level error minimization.
Gait & Posture
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