HUMAN MOVEMENT BIOMECHANICS LAB

Research

General Research Areas

Digital gait biomarkers for early detection of cognitive impairment.

    Alzheimer’s disease and related dementias pose an immediate and growing threat to the health and quality of life for millions of older adults. Interventions for delaying onset are improved with early detection and digital biomarkers based on gait are emerging as key indicators. We have projects investigating the effects of cognition on motor control towards the development of new digital biomarkers of cognitive impairment.

Prevention and rehabilitation of musculoskeletal injuries.

    In particular, hamstring and ACL injuries. We have projects investigating mechanisms of injury, assistive devices for rehabilitation and prevention of hamstring injuries; the relationship between pain, gait, and recovery; and the utility of remote gait analysis for improving rehabilitation outcomes.

    Publications
    1. Gurchiek, RD, Teplin, Z, Falisse, A, Hicks, JL, Delp, SL. Hamstrings are stretched more and faster during accelerative running compared to speed-matched constant speed running. bioRxiv, doi: doi.org/10.1101/2024.03.25.586659.
    2. Gurchiek, RD, Beynnon, BD, Agresta, CE, Choquette, RH, McGinnis, RS, 2021. Wearable sensors for remote patient monitoring in orthopedics: A narrative review. Minerva Orthopedics, 72(5):484-97.
    3. Gurchiek, RD, Choquette, RH, Beynnon, BD, Slauterbeck, JR, Tourville, TW, Toth, MJ, McGinnis, RS, 2019. Open-source remote gait analysis: A post-surgery patient monitoring application. Scientific Reports, 9(1): 17966.

Sparse-sensor human movement analysis.

    We leverage wearable sensing and remote monitoring in almost every project. Practical deployment of these tools requires imposing a minimal burden to the user and, consequently, minimal sensor sets. That is, sparse sensing: we don’t have all the information we’d like to compute the variables of interest. We are investigating solutions at the intersection of machine learning and biomechanical modeling and simulation.

    Publications
    1. Hafer, JF, Vitali, R, Gurchiek, RD, Curtze, C, Shull, P, Cain, SM, 2023. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. Journal of Biomechanics, 157: 111714.
    2. Wearables-only analysis of muscle and joint mechanics: An EMG-driven approach. IEEE Transactions on Biomedical Engineering, 69(2):580-9.
    3. Gurchiek, RD, Ursiny, AT, McGinnis, RM, 2020. A Gaussian process model of muscle synergy functions for estimating unmeasured muscle excitations using a measured subset. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(11): 2478-87.
    4. Gurchiek, RD, Garabed, CP, McGinnis, RS, 2020. Gait event detection using a thigh-worn accelerometer. Gait and Posture, 80: 214-6.
    5. Gurchiek, RD, Cheney, N, McGinnis, RS, 2019. Estimating biomechanical time-series with wearable sensors: A systematic review of machine learning techniques. Sensors, 19(23): 5227.
    6. Gurchiek, RD, McGinnis, RS, Needle, AR, McBride, JM, van Werkhoven, H, 2018. An adaptive filtering algorithm to estimate sprint velocity using a single inertial sensor. Sports Engineering, 21(4): 389-99.
    7. Gurchiek, RD, McGinnis, RS, Needle, AR, McBride JM, van Werkhoven, H, 2017. The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks. Journal of Biomechanics, 61: 263-8.

Creative Inquiries

    • Remote Human Movement Analysis
    • Musculoskeletal Rehabilitation