Research in robotics has recently broadened its traditional focus on industrial applications to include natural, human-like systems. The human musculoskeletal system has over 600 muscles and 200 joint degrees-of-freedom that provide extraordinary flexibility in tailoring its overall configuration and dynamics to the demands of different tasks. The importance of understanding human movement has spurred efforts to build systems with similar capabilities and has led to the construction of actuators, such as pneumatic artificial muscles, that have properties similar to those of human muscles. However, muscles are far more complex than these robotic actuators and will require new control perspectives.
Specifying how to encode high degree-of-freedom muscle functions in order to recreate such movements in anthropomorphic robotic systems is an imposing challenge. This dissertation attempts to advance our understanding by modeling the workings of human muscles in a way that explains how the low temporal bandwidth control of the human brain could direct the high temporal bandwidth requirements of the human movement system. We extend the motor primitives model, a popular strategy for human motor control, by coding a fixed library of movements such that their temporal codes are pre-computed and can be looked up and combined on demand. In this dissertation we develop primitives that lead to various smooth, natural human movements and obtain a sparse-code representation for muscle fiber length changes by applying Matching Pursuit on a parameterized representation of such movements. We employ accurate three-dimensional musculoskeletal models to simulate the lower body muscle fiber length changes for multiple repeatable movements captured from human subjects. We recreate the length changes and show that the signal can be economically encoded in terms of discrete movement elements. Each movement can thus be visualized as a sequence of coefficients for temporally displaced motor primitives.
The primary research contribution of describing movements as a compact code develops a clear hierarchy between the spinal cord and higher brain areas. The code has several other advantages. First, it provides an overview of how the elaborate computations in abstract motor control could be ‘parcellated’ into the brain’s primary subsystems. Second, its parametric description could be used in the extension of learned movements to similar movements with different goals. Thirdly, the sensitivity of the parameters can allow the differentiation of very subtle variations in movement. This research lays the groundwork for understanding and developing further human motor control strategies and provides a mathematical framework for experimental research. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/19530 |
| Date | 22 February 2013 |
| Creators | Iyer, Rahul R. |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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