Invited Speaker: Dr. Ken Loh
Dr. Ken Loh is the TaylorMade Golf Chancellor’s Endowed Professor in the Departments of Structural Engineering, Chemical & Nano Engineering, and Materials Science & Engineering at UC San Diego. He is the Director of the Active, Responsive, Multifunctional, and Ordered-materials Research (ARMOR) Lab and the Jacobs School of Engineering, Center for Extreme Events Research (CEER). He is also an affiliate faculty member of the Center for Wearable Sensors. Dr. Loh received his B.S. in Civil Engineering from Johns Hopkins University in 2004. His graduate studies were at the University of Michigan, where he completed two M.S. degrees in Structural Engineering (2005) and Materials Science & Engineering (2008), as well as a Ph.D. in Structural Engineering in 2008. He started his career in academia in December 2008 as an Assistant Professor and then Associate Professor at UC Davis, before joining UC San Diego in January 2016. His research interests are in nanocomposites, wearable sensors, and metamaterials for solving problems in human performance, structural sustainment, and human-structure interactions. Dr. Loh is also an Engineering Duty Officer in the U.S. Navy Reserve and a co-founder of a start-up, JAK Labs, Inc.
Title of Presentation: Elastic Fabric Motion Tape Sensors for Human Performance Assessment
We live to move, whether it is for work, recreation, or to carry out day-to-day tasks and duties. In terms of sports and the military, improving and optimizing how we move can result in better performance, such as hitting a golf ball more accurately or demonstrating excellent marksmanship. This presentation discusses the development of an individualized Human Digital Twin that uses machine learning algorithms to process novel wearable sensor data for identifying movement deficiencies and providing user-specific feedback. A self-adhesive, elastic fabric, nanocomposite, skin-strain sensor called “Motion Tape” was developed, tested in controlled laboratory environments, and validated through human subject studies. Motion Tape is not only able to measure skin-strains during functional movements, but their measurements are also correlated with how muscles engage. Participants wore Motion Tapes at major muscle groups, and exercises that simulated sports and military training activities were performed. Then, machine learning algorithms were implemented and trained using labeled Motion Tape datasets, specifically, to classify movement sequences and to detect anomalies associated with poor performance. The information derived could be used as direct feedback to augment behavior for improving performance.