The purpose of this investigation was to determine the accuracy of gait metrics measured by Plantiga insoles, as compared to an instrumented treadmill.
The experiments were conducted with participants wearing Plantiga insoles and performing walking and running activities on an instrumented treadmill. Data were collected and processed for both the sensors and the treadmill, and gait metrics were computed for both. Results showed that metrics were predicted with the maximum error IQR of 1.6% and 6.1% for single leg and double leg metrics respectively.
Plantiga sensor insoles provide a simple, accurate, and portable way of quantifying human gait at a fraction of the cost associated with fixed instrumented treadmills.
Gait describes limb motion during locomotion. Human gait (walking and running) tells a great deal about strength, performance, health, alignment, and underlying impairments. That makes quantifying gait very valuable in many contexts. More information on gait can be found in [Capturing gait and jump data with Plantiga Insoles ].
Selected gait metrics measured by Plantiga’s insole system are highlighted below:
Stance Duration
Also known as Ground Contact Time, this is the duration of time that the foot spends on the ground during a single stride. It is measured from heel strike to toe off on the same foot. This metric is useful in identifying abnormal gait patterns, such as limping.
Swing Duration
This is the duration of time that a foot spends in the air during a single stride. It is measured from toe off to heel strike on the same foot. This metric is useful in identifying abnormal gait patterns.
Double Support Percentage
This is the percentage of the time spent with both feet on the ground during a gait cycle. This metric is useful for assessing balance ability and confidence in balance ability.
Single Support Percentage
This is the percentage of the time spent with only one foot on the ground during a gait cycle. It is the inverse of Double Support Percentage.
Flight Ratio
This is the ratio of ground contact time to flight time while running. It is useful as a proxy for vertical oscillation and determining running style, for example, “flatter” vs. “bouncier gaits”.
Plantiga insoles utilize a state-of-the-art, custom built Machine Learned Model to detect and classify gait patterns into segments with millisecond accuracy. The model is able to detect when the feet touch the ground and when they become airborne. This information is then fed into the Gait Quantification Model to compute Gait Metrics. For more information on the ML Model, see [Ground Interaction Algorithm Validation]