Grace McConnochie

Grace McConnochie

Higher Degree by Research Candidate

Adelaide Medical School

Faculty of Health and Medical Sciences

  • Journals
    Year Citation
    2025 McConnochie, G., Fox, A., Badger, H., Bellenger, C., & Thewlis, D. (2025). Fatigue assessment in distance runners: A scoping review of inertial sensor-based biomechanical outcomes and their relation to fatigue markers and assessment conditions. Gait and Posture, 115, 21-33.
    DOI Scopus1 WoS1 Europe PMC1
    2025 McConnochie, G., Fox, A., Bellenger, C., & Thewlis, D. (2025). LiDAR-based scaling of OpenSim musculoskeletal human models is a viable alternative to marker-based approaches - A preliminary study.. J Biomech, 179, 112439.
    DOI Scopus2 WoS2 Europe PMC3
    2025 McConnochie, G., Fox, A. S., Bellenger, C., & Thewlis, D. (2025). Optimal control simulations tracking wearable sensor signals provide comparable running gait kinematics to marker-based motion capture. Peerj, 13(3), 27 pages.
    DOI

  • Conference Items
    Year Citation
    2022 Millar, S., O'Rielly, J., McConnochie, G., Blencowe, M., Turner, L., Wiederman, S., . . . Thewlis, D. (2022). Moving from the lab to the field: accuracy and reliability of wearable sensors in the estimation of lower limb joint kinematics. Poster session presented at the meeting of Defence Human Sciences Symposium.

  • Datasets
    Year Citation
    - McConnochie, G. (n.d.). Dataset: Optimal Control Simulations Tracking Wearable Sensors Provides Comparable Running Gait Kinematics to Marker-Based Motion Capture.
    DOI
    - McConnochie, G. (n.d.). Dataset: Optimal Control Simulations Tracking Wearable Sensors Provides Comparable Running Gait Kinematics to Marker-Based Motion Capture.
    DOI

  • Software
    Year Citation
    - McConnochie, G. (n.d.). Dataset: Optimal Control Simulations Tracking Wearable Sensors Provides Comparable Running Gait Kinematics to Marker-Based Motion Capture [Computer Software].
    DOI
Connect With Me
External Profiles