- Charles Stark Draper Professor of Aeronautics and Astronautics
Leia Stirling received a BS and MS in Aeronautics and Astronautics from the University of Illinois at Urbana-Champaign in 2003 and 2005, respectively, and a PhD in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2008. She was then a postdoctoral fellow from 2008-2009 at Boston Children’s Hospital and Harvard Medical School. In 2009, she joined the Wyss Institute for Biologically Inspired Engineering at Harvard University as a member of the Advanced Technology Team and Director of the Motion Capture Lab. She joined the Faculty at MIT in the fall of 2013 as an Assistant Professor in the Department of Aeronautics and Astronautics.
- PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology, 2008
- MS in Aeronautics and Astronautics, University of Illinois at Urbana-Champaign, 2005
- BS in Aeronautics and Astronautics, University of Illinois at Urbana-Champaign, 2003
- American Society of Biomechanics
- Gait and Clinical Movement Analysis Society
- Member, IEEE
- NSF Faculty Early Career Development (CAREER) Award
- MIT AIAA Undergraduate Advisor Award
Professor Stirling’s research interests span computational dynamics, human-machine interaction, system automation, signal processing, human factors, and experimental biomechanics. She applies these interests to the development of tightly coupled human-machine systems, including wearable technology. In particular, she develops the capability for wearable technology to be used by nonexperts to robustly measure physiological signals to augment clinical capability and to enable individuals to become their own advocates for their health. Research problems include characterizing the underlying variability of relevant functional tasks, developing physics-based algorithms and performance metrics that are robust to these underlying uncertainties, and developing decision making tools synergistic with the end user.
It is possible to approach each of these problems independently. However, these problems are all linked in that they involve an understanding of the human in a natural environment, use measured data to infer an aspect of the human system, and then must be presented to a decision maker (human or robotic system) and used appropriately. The design and validation of algorithms and models are dependent on the data collected during the development phase. Knowing the data must be presented to a nonexpert may affect the selection of performance metric and thus how the algorithms are designed. Research from the human-centered viewpoint for decision making involves the understanding of the human in his or her actual environment, including the quantification of task variability, external confounding stimuli, the formalization of relevant expert knowledge, and presentation of the data to the decision maker. Through this unified path, the Stirling Group approaches a wide spectrum of applications related to health and performance monitoring and system design, including for the astronaut, soldier, clinician, and general consumer.
- L. Stirling, L. Lewis, M. Qureshi, D. Kelty-Stephen, A. Goldberger, and M. Costa. “Use of a Tracing Task to Assess Visuomotor Performance: Effects of Age, Sex, and Handedness.” Journals of Gerontology: Series A Biol Sci Med Sci 68.8 (2013): 938-45.
- Anticipatory Signals in Kinematics and Muscle Activity During Functional Grasp and Release.” IEEE Body Sensor Networks. Boston, MA, 2015. “
- Human-Robot Co-Navigation using Anticipatory Indicators of Human Walking Motion.” IEEE International Conference on Robotics and Automation (ICRA). Seattle, WA, 2015. “
- A pediatric robotic thumb exoskeleton for at-home rehabilitation: The Isolated Orthosis for Thumb Actuation.” International Journal of Intelligent Computing and Cybernetics 7.3 (2014): 233-52. “
- Wearable Soft Sensing Suit for Human Gait Measurement.” International Journal of Robotics Research 33.14 (2014): 1748-64. “
A full list of Professor Stirling’s publications can be found on her website.
- 16.400/16.453J/HST.518J: Human Systems Engineering
- 16.470J/ESD.756J: Statistical Methods in Experimental Design
- 16.09: Statistics and Probability