Project Description

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. 

Start Date

May, 2023

Postdoc Qualifications

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

Co-Advisors

Laura Blumenschein, School of Mechanical Engineering;
lhblumen@purdue.edu, https://engineering.purdue.edu/raad
Deva Chan, Weldon School of Biomedical Engineering;
chand@purdue.edu, https://engineering.purdue.edu/ChanLab

Bibliography

• Chan DD, Toribio D, Neu CP. Displacement smoothing for the precise MRI-based measurement of strain in soft biological tissues. Comput Methods Biomech Biomed Engin. 2013;16(8):852-60. doi: 10.1080/10255842.2011.641178. Epub 2012 Jan 31. PMID: 22292493.
• Chan DD, Cai L, Butz KD, Trippel SB, Nauman EA, Neu CP. In vivo articular cartilage deformation: noninvasive quantification of intratissue strain during joint contact in the human knee. Sci Rep. 2016 Jan 11;6:19220. doi: 10.1038/srep19220. PMID: 26752228; PMCID: PMC4707486.
• Cagnie B, Elliott J, O'Leary S, D'hooge R, Dickx N, Danneels L. Muscle functional MRI as an imaging tool to evaluate muscle activity. Journal of orthopaedic & sports physical therapy. 2011 Nov;41(11):896-903.
• Mcdonald CG, Fregly BJ, O’Malley MK. Effect of robotic exoskeleton motion constraints on upper limb muscle synergies: A case study. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2021 Oct 7;29:2086-95.
• Awad LN, Kudzia P, Revi DA, Ellis TD, Walsh CJ. Walking faster and farther with a soft robotic exosuit: Implications for post-stroke gait assistance and rehabilitation. IEEE open journal of engineering in medicine and biology. 2020 Apr 2;1:108-15.