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An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns
Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland
School of Public Health, Physiotherapy and Population Science, University College Dublin, Health Sciences Centre, Belfield, Dublin 4, Ireland
Institute for Sport and Health, University College Dublin, Dublin 4, Ireland
* Author to whom correspondence should be addressed.
Received: 30 November 2013; in revised form: 20 December 2013 / Accepted: 26 December 2013 / Published: 7 January 2014
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.
Keywords: locomotion; inertial sensor; gyroscope; knee joint; feature extraction; wearable sensor
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MDPI and ACS Style
Patterson, M.R.; Delahunt, E.; Sweeney, K.T.; Caulfield, B. An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns. Sensors 2014, 14, 887-899.
Patterson MR, Delahunt E, Sweeney KT, Caulfield B. An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns. Sensors. 2014; 14(1):887-899.
Patterson, Matthew R.; Delahunt, Eamonn; Sweeney, Kevin T.; Caulfield, Brian. 2014. "An Ambulatory Method of Identifying Anterior Cruciate Ligament Reconstructed Gait Patterns." Sensors 14, no. 1: 887-899.