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Updated: Aug 29, 2025

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Temporal Variation Quantification During Cognitive Dual-Task Gait Using Two IMU Sensors.

Yonatan Hutabarat, Dai Owaki, Mitsuhiro Hayashibe

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
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    Wearable sensors on shoes can detect subtle changes in walking patterns during dual-task gait, like smartphone use. This technology helps analyze distracted walking outside the lab.

    Area of Science:

    • Biomechanics
    • Human-computer interaction
    • Wearable technology

    Background:

    • Daily walking often involves performing secondary tasks, leading to 'dual-task gait'.
    • Distracted walking, particularly with smartphones, is a growing societal concern.
    • In-lab gait analysis may not represent real-world walking conditions.

    Purpose of the Study:

    • To evaluate the effectiveness of using two inertial measurement units (IMUs) on shoes for assessing cognitive dual-task gait.
    • To differentiate dual-task gait from single-task gait using wearable sensors.
    • To explore the sensitivity of shoe-mounted IMUs in detecting gait alterations.

    Main Methods:

    • Utilized two inertial measurement units (IMUs) attached to the shoes.
    • Compared gait parameters during single-task walking versus cognitive dual-task walking.

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    Last Updated: Aug 29, 2025

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    Revised and Neuroimaging-Compatible Versions of the Dual Task Screen
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  • Focused on out-of-lab, real-world gait analysis.

    • Main Results:

    • The proposed system effectively identified subtle changes in gait features during dual-task walking.
    • Significant alterations were observed in double support time and gait indices.
    • The system demonstrated sensitivity to cognitive load's impact on gait.

    Conclusions:

    • Shoe-mounted IMUs offer a viable, out-of-lab solution for assessing dual-task gait.
    • This approach can accurately detect gait modifications caused by cognitive demands.
    • The findings contribute to understanding and monitoring distracted walking behaviors.