Real-Time Measurement of Human Tissue Changes Induced by Wearable Devices
|Interdisciplinary Areas:||Engineering-Medicine, Human-Machine/Computer Interaction, Human Factors, Human-Centered Design
Understanding how humans can be influenced to improve their body’s motions is an active area of research with applications across the full scale of abilities; from improving athletic performance, to preventing injury on worksites, to rehabilitating those with motor impairments. Research into human motor learning presents two unique challenges, (1) understanding how to build devices to improve human motion and (2) understanding how to measure the improvements in human motion. In previous work, answering these questions take significant time as outward improvements in human motion can take months of intervention to appear and the process of designing wearable active and passive devices often involves multiple rounds of prototyping. In this work, we aim to speed up the design and analysis process. We leverage techniques within soft wearable systems to increase the rate of design cycles and investigate in-vivo muscle measurements like MRI to measure and relate muscle changes within minutes of initial intervention to long term motor improvements. This work will support fundamental understanding of the processes underlying motor improvement and will lead to new technologies that can help the world move better.
The ideal candidate will be able to demonstrate…
• A Ph.D. in Biomedical, Biomechanical, Mechanical Engineering, or closely related field
• Expertise in either (i) wearable devices and soft robotic design, or (ii) soft tissue mechanics and/or biomechanics modeling; as well as a willingness to learn
• Ability to independently design, conduct, and document experiments
• Critical thinking, curiosity, and creativity in multidisciplinary research
• Effective project and time management, mentorship, leadership, and interpersonal skills
• Strong oral and written communication
Laura Blumenschein, School of Mechanical Engineering;
Deva Chan, Weldon School of Biomedical Engineering;
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