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Final Program


Final Congress Guide

Biomedical Sciences

100 Section - Quantifying sway: low- and high-frequency postural events across static standing tasks using a cervical-mounted inertial measurement unit

Wednesday, May 20, 2026
6:00 PM - 7:30 PM PT

    Presenting Author/Autor expositor(s)

  • MP

    Mohit Prashar, n/a

    Research Assistant
    KITE Research Institute, University Health Network
    Brampton, Ontario, Canada

    • Co-Author/Coautor(s)

  • AZ

    Abdullah Zafar, MSc

    PhD Student
    Université de Montréal – Department of Biomedical Sciences
    Montréal, Quebec, Canada

  • KZ

    Karl Zabjek, PhD

    Associate Professor
    University of Toronto – Temerty Faculty of Medicine, Department of Physical Therapy
    Toronto, Ontario, Canada

  • BC

    Beverley Craven, MD, MSc, FRCPC

    Professor
    University of Toronto – Temerty Faculty of Medicine, Department of Medicine
    Toronto, Ontario, Canada

  • TW

    Thomas Walden, PhD

    Postdoctoral Research Fellow
    KITE Research Institute, University Health Network
    Toronto, Ontario, Canada

Objectives : Optimal distribution of mechanical load and active movement of the center of mass (COM) over the base of support during upright posture are essential. However, traditional metrics fail to capture the frequency dynamics of postural sway under varying stance conditions. Therefore, the proof-of-concept study aimed to characterize the low- and high-frequency dynamics of COM movement across stance conditions via inertial measurement unit (IMU).

Design:

Sixteen adults completed three stance configurations (standing unsupported, tandem stance (left and right), and one-leg stance (left and right) for 20-seconds each with an IMU positioned at the C7 spinous process. Net acceleration (X, Y, Z-axes) was filtered using 4th-order Butterworth filters (low-pass < 0.7 Hz; bandpass 1-20 Hz) to isolate low- and high-frequency components. Peak detection algorithm identified low-frequency peaks (≥1s apart, prominence ≥0.02m/s²) and high-frequency peaks (120–300ms apart), including amplitude measures (max, min, mean). Pearson correlation examined the relationship between stance difficulty and event rates across frequency bands.

Results:

In the unsupported stance, low-frequency analysis detected 5-13 peaks per trial (mean amplitude = 8.47 m/s²), while high-frequency analysis detected 117-226 peaks (mean amplitude (0.025 m/s²). In tandem stance, low-frequency peaks increased to 10-25 per trial (mean = 8.35 m/s²), and high-frequency peaks ranged 79-195 (mean = 0.043 m/s²). In the one-leg stance, low-frequency peaks reached 8-28 per trial (mean = 8.61 m/s²), whereas high-frequency peaks decreased to 60-212 (mean = 0.066 m/s²). As stance difficulty increased, low-frequency event rates increased (r = 0.53) and high-frequency rates decreased (r = -0.33).

Conclusion:

This proof-of-concept demonstrates the feasibility of using a cervical-mounted IMU to quantify frequency-specific postural adjustments across varying stance demands. The observed relationship between task difficulty and peak frequencies indicates a shift from fast, fine-tuned corrections to slower, larger postural adjustments.