‍Capturing the Natural, Unconscious Gait Pattern

_{Traditional gait analysis often captures a "performance" rather than a patient's everyday mobility. This research utilizes smartphone-based gait analysis to enable the passive collection of gait data as people walk with their natural, unconscious gait pattern. By comparing these "background" walk data to consciously recorded "in-app" measurements, clinicians can finally differentiate between a patient's maximum capacity and their "real" walk pattern.}

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The "Healthy Shift" and Correlation of Gait Parameters

_{The study found that after proper aggregation, conscious and unconscious gait are highly correlated for most parameters. However, the data reveals that unconscious gait tends to have parameter values that are, on average, slightly shifted in the direction corresponding to less healthy gait. Furthermore, a small number of patients show drastic differences between the two, indicating that their conscious recordings do not represent how they move in the real world.}

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Heightened Visibility into Recovery Trajectories

_{Beyond simple snapshots, the use of background data allows for heightened visibility and clinical insights into a patient’s recovery trajectory after surgery or during physical therapy. This research demonstrates how continuous, passive monitoring captures the nuances of progress that a single in-office test might miss. By analyzing how a patient walks when they aren't thinking about it, providers can offer more accurate, data-driven interventions tailored to a patient's true functional status.}

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Unlock the Full Clinical Paper

_{‍Discover the data behind background walk data and learn how capturing the unconscious gait pattern provides a more honest view of patient health and recovery.}