Abstract
The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.
Highlights
Inertial sensor technology has the potential to bring gait analysis outside of the biomechanics laboratory and allow gait analysis to be more accessible to a wider group of clinicians and researchers on a more regular basis [1,2]
Instead of gait analysis being confined to complex biomechanics laboratories, as with traditional gait analysis tools, gait analysis with inertial sensors could take place during regular clinical check-ups, in the home and ubiquitously as people go about their daily lives [5]
A significant difference was found between the groups in three of the gyroscope variables: peak rotation rate at mid-swing for the anterior cruciate ligament reconstructed (ACL-R) group was significantly lower than the control group; minimum rotation rate at IC for the ACL-R group was significantly lower than the control group; post-Heel strike (HS) shank adjustments for the ACL-R group were significantly lower than the control group
Summary
Inertial sensor technology has the potential to bring gait analysis outside of the biomechanics laboratory and allow gait analysis to be more accessible to a wider group of clinicians and researchers on a more regular basis [1,2]. Instead of gait analysis being confined to complex biomechanics laboratories, as with traditional gait analysis tools, gait analysis with inertial sensors could take place during regular clinical check-ups, in the home and ubiquitously as people go about their daily lives [5] Such inertial sensor use could drastically increase the amount of gait data that clinicians and researchers can obtain. Despite the current depth of research in the inertial sensor gait area, the sensors are not used extensively outside of the laboratory environment, save for example as a step counter This may be partly due to the fact that inertial sensor gait analysis use has been primarily confined to replication of traditional gait analysis metrics; such as temporal, spatial and kinematic data. ACL-R participants provide a novel test cohort to determine if inertial sensors can be used to detect gait changes that are not obvious to the eye
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