MECHENG 7385
Transcript Abbreviation:
Advan Human Movmnt
Course Description:
Mathematical ideas from control theory, optimization, nonlinear dynamics, and numerical computation that will be used to understand human, animal, and movement behavior and design.
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 2030 (430), Math 2174, 2415 (415), 4568 (568), or 571, or Grad standing in Engineering.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Learn about a various control theoretic and mathematical methods that are useful more broadly that for just the study of human and animal movement.
Learn about various theories of human and animal movement, including optimality and robustness and their use in building better robots.
Learn how to implement these ideas and theories to make predictions using computational techniques in MATLAB.
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Legged locomotion, various experiments, various simple mathematical models, energetics, and stability. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Stability: Notions of stability, eigenvalues, return maps, passive dynamic robots, actively controlled biped models, various control techniques, etc. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Optimal trajectory control: Finding motions that minimize energy, or maximize other performance measures. Optimal walking and running patterns, optimal athletic movements, etc. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Brief introduction to optimal feedback control, stochastic optimal control, and robustness. Finding body motions that are robust to external perturbations and internal muscle noise. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Bayesian and other optimal sensory integration. Estimation theory, in brief. How do animals know what’s going on? Sensory illusions. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Learning theories in brief. | 0.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 0 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework, involving mathematical analytical and computational techniques applied to problems in biomechanics. | 50% |
| Project, involving the construction of a simulation or analysis of some aspect of human or animal movement, or finding an optimal movement | 50% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: