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Related Experiment Video

Updated: May 6, 2026

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
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Smart Socks for Gait Analysis: A Comparative Study of Algorithms.

F Colelli Riano, F Amitrano, G Iaselli

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary
    This summary is machine-generated.

    This study compared Sensoria Smart Socks and APDM Mobility Lab systems for gait analysis. Higher cut-off frequencies improved agreement for stance and swing parameters, showing potential for wearable rehabilitation devices.

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    Area of Science:

    • Biomedical Engineering
    • Sports Science
    • Rehabilitation Technology

    Background:

    • Wearable e-textile systems offer promising solutions for remote patient monitoring and rehabilitation.
    • Accurate extraction of spatio-temporal gait parameters is crucial for assessing mobility and guiding interventions.
    • Validation of novel wearable sensors against established reference systems is essential for clinical adoption.

    Purpose of the Study:

    • To evaluate the agreement between Sensoria Smart Socks and APDM Mobility Lab systems for gait analysis.
    • To assess the impact of different Butterworth filtering cut-off frequencies on spatio-temporal parameter extraction.
    • To determine the optimal filtering strategy for reliable gait data acquisition using e-textile technology.

    Main Methods:

    • Experimental data collected from eighteen healthy subjects during gait analysis.
    • Gait parameters including gait cycle time, cadence, swing, and stance were statistically analyzed.
    • Signals were conditioned using Butterworth filtering with varying cut-off frequencies.

    Main Results:

    • Strong agreement observed for gait cycle time and cadence, irrespective of filtering method.
    • Higher cut-off frequencies or no filtering enhanced agreement for stance and swing parameters.
    • Lower cut-off frequencies introduced significant systematic errors in stance and swing measurements.

    Conclusions:

    • Sensoria Smart Socks demonstrate good agreement with the APDM Mobility Lab for key gait parameters.
    • Filtering strategies significantly impact the accuracy of stance and swing measurements.
    • Optimized filtering, potentially through hybrid algorithms, is recommended for accurate gait analysis with wearable e-textile systems, supporting their use in rehabilitation.