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Design & Manage Biomechanical Uncertainty

Dr. Simon Hsiang, PhD, PE, CPE

Department Chair, Systems Engineering & Engineering Management

University of North Carolina at Charlotte

Friday, November 2, 2018

2:30-3:30 JDT 410


The occupational biomechanics aims to understand the mechanics of human bodies in complex systems, and the challenge is to provide the in-situ evidence reflecting the human behaviors in the system of interest under different anthropometrical, psychophysical and psychosocial constraints. This talk describes the fundamental and conceptual aspects of the biomechanical and behavioral uncertainty.  To demonstrate the aspects three projects will be used: the multi-objective trade-off in manual materials handling, the optimal control and stochastic resonance in VR walkway design, and the computational model for spacecraft/habitat volume.  Through these studies some basic questions regarding how to design and manage biomechanical uncertainty will be examined: Can we mine data, design experiment, or perform certain analysis or simulation to validate or falsify crucial assumptions? What explicit and implicit assumptions have we made? Have we confused facts with behavioral assumptions? How would the design or control change if each of our key assumptions proves incorrect? What are the specifics in the situation or operating condition?   What concerns remain uncertain or ambiguous?


Dr. Simon M. Hsiang is the Department Chair and a professor of Systems Engineering and Engineering Management at University of North Carolina at Charlotte.   Prior to joining UNC Charlotte in July 2015, he served as the E. L. Derr Professor at Texas Tech University.  He has more than 20 years of teaching experience in the areas of biomechanics and data analytics. He was a senior researcher for the Liberty Mutual Insurance Group in Boston for 8 years working in the areas of incident analysis and field surveys of risk factors involved in occupational injuries and worker compensation. The risk analysis involved in his work are usually multi-features, multiple failure modes, highly demographical and geographical networks. Since he joined academia, he has received support from the National Institute of Occupational Safety and Health (NIOSH), the U.S. Postal Service (USPS), the U.S. Air Force and the National Aeronautics and Space Administration (NASA). His past experience with large complex systems of recurring events in the actuarial and field epidemiological studies can be directly conveyed to the stratification and development of meta-rules. The goals for both systems are the same, to transfer community dependent incomplete anecdotes into verifiable probability based on the given causal criteria.