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Feet-Based IMU Algorithm Yields High Specificity for Detection of Walking in Daily Life.

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    A new algorithm using inertial measurement units (IMUs) on both feet accurately detects walking periods in daily life. This method offers high specificity for monitoring individuals with gait impairments.

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    Area of Science:

    • Biomechanics
    • Wearable Technology
    • Gait Analysis

    Background:

    • Ecologically valid gait characteristics are crucial for monitoring individuals with gait impairments.
    • Accurate selection of walking periods from long-term recordings is a prerequisite for gait analysis.

    Purpose of the Study:

    • To develop and validate a novel algorithm for walking detection using inertial measurement units (IMUs) on both feet.
    • To compare the performance of the "Both feet" algorithm against existing methods using sacrum accelerometer and single foot gyroscopic data.
    • To identify daily activities that may compromise the specificity of walking detection algorithms.

    Main Methods:

    • An algorithm utilizing IMUs on both feet was developed, involving stages for potential walking period selection, exclusion of non-walking periods, and verification of minimal walking bout requirements.
    • Algorithm performance (specificity, sensitivity, accuracy) was validated in 32 participants (12 healthy, 20 with neurological gait impairment) during 20-30 minutes of daily activities, using video recordings as ground truth.
    • Comparison was made with algorithms using sacrum accelerometer data and single foot gyroscopic data.

    Main Results:

    • The "Both feet" algorithm demonstrated superior specificity (96.6%) compared to "Single foot" (92.1%) and "Sacrum" (72.1%) algorithms.
    • Stair climbing was the activity most frequently misclassified as walking across all tested algorithms.
    • While the "Sacrum" algorithm achieved higher sensitivity (99.5%), it exhibited lower specificity and accuracy.

    Conclusions:

    • The "Both feet" algorithm's high specificity makes it suitable for obtaining spatiotemporal gait characteristics in daily life.
    • This algorithm is applicable for monitoring individuals with mild neurological conditions affecting gait.
    • Further research should address the misclassification of activities like stair climbing.