We are proud to announce that Dr. Katherine M. Steele was selected as one of the 2020 DO-IT Trailblazers for her fantastic work in access engineering. Kat has been active in DO-IT, hosting activities where students learn about makerspace accessibility, engineering principles, and universal design. She has also developed resources and published articles with DO-IT staff on topics related to makerspace accessibility and teaching about accessibility in engineering. Please help us in congratulating Kat!
We are proud to announce that Momona Yamagami was selected as the winner of the first annual CNT (Center for Neurotechnology) Fernando Family Fund Best Student Paper award for her paper titled, “Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance”. The best paper award was selected based on its significance and potential impact, its technical content, the originality of the proposed research, and the clarity of the solutions presented. Congratulations to Momona!
How can we decipher human movement?
Dr. Steele presents at the inaugural research symposium for the University of Washington Center for Translational Muscle Research. Her presentation shares examples for how we can use musculoskeletal simulation as a tool to connect muscle biology, dynamics, and mobility.
Journal Article in The Royal Society:
This work highlights the potential of data-driven models grounded in dynamical systems theory to predict complex individualized responses to ankle exoskeletons., without requiring explicit knowledge of the individual’s physiology or motor control
Aim: Evaluate the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics.
Method: Data from 12 unimpaired adults walking with bilateral passive ankle exoskeletons were captured. PV, linear PV (LPV), and nonlinear PV (NPV) models leveraged Floquet theory to kinematics and muscle activity in response to three exoskeleton torque conditions.
Results: The LPV model’s predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49–70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses.
Interpretation: This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.