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  • Author: Kevin J. Deluzio x
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Marcus J. Brown, Laura A. Hutchinson, Michael J. Rainbow, Kevin J. Deluzio and Alan R. De Asha

A typical gait analysis data collection consists of a series of discrete trials, where a participant initiates gait, walks through a motion capture volume, and then terminates gait. This is not a normal ‘everyday’ gait pattern, yet measurements are considered representative of normal walking. However, walking speed, a global descriptor of gait quality that can affect joint kinematics and kinetics, may be different during discrete trials, compared to continuous walking. Therefore, the purpose of this study was to investigate the effect of continuous walking versus discrete trials on walking speed and walking speed variability. Data were collected for 25 healthy young adults performing 2 walking tasks. The first task represented a typical gait data collection session, where subjects completed repeated trials, beginning from a standstill and walking along a 12-m walkway. The second task was continuous walking along a “figure-of-8” circuit, with 1 section containing the same 12-m walkway. Walking speed was significantly higher during the discrete trials compared to the continuous trials (p < .001), but there were no significant differences in walking speed variability between the conditions. The results suggest that choice of gait protocol may affect results where variables are sensitive to walking speed.

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Christopher M. Saliba, Allison L. Clouthier, Scott C.E. Brandon, Michael J. Rainbow and Kevin J. Deluzio

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a noninvasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis, and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) root mean square differences of 0.17 (0.05) bodyweight compared with the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

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Silvia Cabral, Renan A. Resende, Adam C. Clansey, Kevin J. Deluzio, W. Scott Selbie and António P. Veloso

High levels of gait asymmetry are associated with many pathologies. Our long-term goal is to improve gait symmetry through real-time biofeedback of a symmetry index. Symmetry is often reported as a single metric or a collective signature of multiple discrete measures. While this is useful for assessment, incorporating multiple feedback metrics presents too much information for most subjects to use as visual feedback for gait retraining. The aim of this article was to develop a global gait asymmetry (GGA) score that could be used as a biofeedback metric for gait retraining and to test the effectiveness of the GGA for classifying artificially-induced asymmetry. Eighteen participants (11 males; age 26.9 y [SD = 7.7]; height 1.8 m [SD = 0.1]; body mass 72.7 kg [SD = 8.9]) walked on a treadmill in 3 symmetry conditions, induced by wearing custom-made sandals: a symmetric condition (identical sandals) and 2 asymmetric conditions (different sandals). The GGA score was calculated, based on several joint angles, and compared between conditions. Significant differences were found among all conditions (P < .001), meaning that the GGA score is sensitive to different levels of asymmetry, and may be useful for rehabilitation and assessment.