Automated structuring of gait data for analysis purposes - A deep learning pilot example

Clinical gait analysis can help diagnose ambulatory children with cerebral palsy and provide treatment recommendations. This group represents the largest group of children with gait problems. Currently, the workflow for 3D gait analysis involves a complex process of collecting motion capture data and other types of data, analyzing the collected data, and creating an expert knowledge-based assessment. With this in mind, a data pipeline is essential for efficiently and effectively structuring data and reducing the time and effort required for data annotation and organization.A novel data pipeline has been developed to help structure, anonymize and automate parts of the annotation process of the data. In this sense, a pilot experiment was conducted using a simple convolutional neural network to classify between hemi-plegic and diplegic gait. This experiment included preprocessing the data, training the model and testing it.The data pipeline was used to create a semi-automated annotated data set. The neural network was trained on the data set and achieved an accuracy of 0.78 and a median of 1.0 on a holdout test set.