Nathalie Oomen

Graduate Student, PhD
Nathali Oomen.

Contact information:


Location: B.C. Matthews Hall, room 1115


I am curious about why people move the way they do, and how that could affect why some people get injured at the workplace while others do not. This motivates me to investigate the repeaters-replacers hypothesis, where repeaters are described by repeated movement behaviour with possible higher risk of injury while replacers show a lot of variety in their movements and thus could have a lower risk of injury. Also, I have a broad interest in applying different signal processing and data science techniques such as machine learning to biomechanics research.

Selected publications

See Google Scholar for full list of publications.

  • Oomen, N. M. C. W., Graham, R. B., & Fischer, S. L. (2022). Exploring the role of task constraints on motor variability and assessing consistency in individual responses during repetitive lifting using linear variability of kinematics. Applied Ergonomics, 100, 103668.
  • Oomen, N.M.C.W., Pegg, C.C., Graham, R.B. & S.L. Fischer. A machine learning model with only two features can accurately classify lifting height and weight based on forearm and pelvis kinematics. XXVII Congress of the International Society of Biomechanics/ 43th Annual meeting of the American Society of Biomechanics. Calgary, Alberta, Canada. July 31 – August 4 2019.
  • Lad, U., Oomen, N.M.C.W., Callaghan, J.P., & Fischer, S.L. (2018). Comparing the biomechanical and psychophysical demands imposed on paramedics when using manual and powered stretchers. Applied Ergonomics, 70:167-174.
  • Oomen, N.M.C.W., Pegg, C., Graham, R.B. & S.L. Fischer. Classification of lifting heights and weights using generic versus targeted feature extraction. The 20th Biennial Meeting of the Canadian Society for Biomechanics. Halifax, Nova Scotia, Canada. 14-17 August 2018.

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