Research Interests

I am interested in designing, testing, and deploying novel rehabilitation strategies to improve human mobility using musculoskeletal simulation, biofeedback, machine learning, and wearables. I currently focus on gait retraining for knee osteoarthritis. I use biofeedback to train individuals with knee osteoarthritis to alter their kinematics or coordination strategies in order to reduce the loading in their diseased joint. I use musculoskeletal simulation, clinical measures, and MR imaging to assess the mechanical and clinical efficacy of these interventions.

Degrees

B.S. in Mechanical Engineering, Baylor University, 2014
M.S. in Mechanical Engineering (Biomechanics focus), Stanford University, 2016

Honors and Awards

David Winter Young Investigator Award Finalist (ISB, Calgary), 2019
Young Investigator Award (OARSI World Congress, Toronto), 2019
De Luca Student Travel Award (WCB, Dublin), 2018
NSF Graduate Research Fellowship, 2015
Stanford Graduate Fellowship, 2014

Publications

Simpson C.S., Welker C.G., Uhlrich S.D., Sketch S.M., Jackson R.W., Delp S.L., Collins S.H., Selinger J.C., Hawkes E.W., 2019. Connecting the legs with a spring improves human running economy. Journal of Experimental Biology, In Press. Download PDF File

Haddock B., Fan A.P., Uhlrich S.D., Jorgensen N., Suetta C., Gold G.E., Kogan  F., 2019. Assessment of acute bone loading in humans using [18F]NaF PET/MRI. European Journal of Nuclear Medicine and Molecular Imaging, In Press. Download PDF File

Uhlrich S.D., Silder A., Beaupré G.S. Shull, P.B. Delp, S.L., 2018. Subject-specific toe-in or toe-out gait modifications reduce the larger knee adduction moment peak more than a non-personalized approach. Journal of Biomechanics 66, p103-110. Download PDF File