Presenter(s)
Ali Almandeel
Files
Download Project (1.3 MB)
Description
Identifying the center of mass location (CoM) provides a significant aid in controlling the balance of humanoid robots. For human beings, the stability of motion is highly influenced by their ability to control their CoM and center of pressure (CoP). Additionally, computing the CoM can prove critical to assessing rehabilitation and in elite sports training. Human body segment parameters can be obtained from anthropometric tables. Their accuracy for a given individual is questioned due to differences in age, race, and fitness level from the sample population. This research presents an estimation technique that uses the statically equivalent serial chain (SESC). A SESC is a representation of any multilink branched chain, like a human or humanoid, whose end-effector locates the CoM. The SESC's construction during an experimental phase depends on the node positions from a motion capture system (like the Microsoft Kinect), and the total mass and CoP from a force plate (like the Wii Balance Board). Additionally, the presence of a static body in the workspace (a walker or chair, for example) to create stability in test subjects is presented. The utility of the presented method as compared to other common methods for CoM estimation is that the force plate is not needed to track the CoM after the SESC is constructed.
Publication Date
4-9-2015
Project Designation
Graduate Research
Primary Advisor
Andrew P. Murray, David H. Myszka
Primary Advisor's Department
Mechanical and Aerospace Engineering
Keywords
Stander Symposium project
Disciplines
Arts and Humanities | Business | Education | Engineering | Life Sciences | Medicine and Health Sciences | Physical Sciences and Mathematics | Social and Behavioral Sciences
Recommended Citation
"Rapidly Locating and Accurately Tracking the Center of Mass Using Statically Equivalent Serial Chains" (2015). Stander Symposium Projects. 674.
https://ecommons.udayton.edu/stander_posters/674
Included in
Arts and Humanities Commons, Business Commons, Education Commons, Engineering Commons, Life Sciences Commons, Medicine and Health Sciences Commons, Physical Sciences and Mathematics Commons, Social and Behavioral Sciences Commons