Autoencoder-derived Single Summary Metric to Assess Gait Quality

Abstract

Research Objectives To improve holistic interpretation of quantitative gait analysis data through a deep learning autoencoder with a single-valued reduced representation. The tailored single value is another strategy to quantify overall gait quality in individuals with movement disorders. Such overall summary metrics provide a holistic assessment of gait quality for assessing outcomes of therapeutic interventions. Design Observational Study. Setting The data was collected at the Motion Analysis Centers (MACs) at the Shriners Hospitals for Children - Chicago. Participants 412 subjects with cerebral palsy under the age of 18 and 86 normal subjects (498 total) with collected gait data from the MAC at Shriners Hospitals for Children - Chicago over 20 years. Interventions Not applicable. Main Outcome Measures A single summary metric generated by an autoencoder model quantifies the quality of an individual's gait. This metric can be used as a measure of improvement of gait after surgery to improve mobility in conditions such as Cerebral Palsy. Results The model is trained using high-quality data at the Shriners MAC in Chicago from 2004 to 2020, which includes temporo-spatial parameters (7), lower extremity kinematic (64), and lower extremity kinetic (43) data - a total of 114 features. The learned single metric captures more than 86% variance, which was established using subject-wise cross-validation. Conclusions This preliminary model can produce an overall summary value for the gait of individuals by incorporating the temporo-spatial parameters, lower extremity kinematics, and lower extremity kinetic data. Such summary metrics can be used to assess the impact of therapeutic interventions. Author(s) Disclosures None. To improve holistic interpretation of quantitative gait analysis data through a deep learning autoencoder with a single-valued reduced representation. The tailored single value is another strategy to quantify overall gait quality in individuals with movement disorders. Such overall summary metrics provide a holistic assessment of gait quality for assessing outcomes of therapeutic interventions. Observational Study. The data was collected at the Motion Analysis Centers (MACs) at the Shriners Hospitals for Children - Chicago. 412 subjects with cerebral palsy under the age of 18 and 86 normal subjects (498 total) with collected gait data from the MAC at Shriners Hospitals for Children - Chicago over 20 years. Not applicable. A single summary metric generated by an autoencoder model quantifies the quality of an individual's gait. This metric can be used as a measure of improvement of gait after surgery to improve mobility in conditions such as Cerebral Palsy. The model is trained using high-quality data at the Shriners MAC in Chicago from 2004 to 2020, which includes temporo-spatial parameters (7), lower extremity kinematic (64), and lower extremity kinetic (43) data - a total of 114 features. The learned single metric captures more than 86% variance, which was established using subject-wise cross-validation. This preliminary model can produce an overall summary value for the gait of individuals by incorporating the temporo-spatial parameters, lower extremity kinematics, and lower extremity kinetic data. Such summary metrics can be used to assess the impact of therapeutic interventions.