6 March 2018 Medical imaging informatics based solutions for human performance analytics
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For human performance analysis, extensive experimental trials are often conducted to identify the underlying cause or long-term consequences of certain pathologies and to improve motor functions by examining the movement patterns of affected individuals. Data collected for human performance analysis includes high-speed video, surveys, spreadsheets, force data recordings from instrumented surfaces etc. These datasets are recorded from various standalone sources and therefore captured in different folder structures as well as in varying formats depending on the hardware configurations. Therefore, data integration and synchronization present a huge challenge while handling these multimedia datasets specifically for large datasets. Another challenge faced by researchers is querying large quantity of unstructured data and to design feedbacks/reporting tools for users who need to use datasets at various levels. In the past, database server storage solutions have been introduced to securely store these datasets. However, to automate the process of uploading raw files, various file manipulation steps are required. In the current workflow, this file manipulation and structuring is done manually and is not feasible for large amounts of data. However, by attaching metadata files and data dictionaries with these raw datasets, they can provide information and structure needed for automated server upload. We introduce one such system for metadata creation for unstructured multimedia data based on the DICOM data model design. We will discuss design and implementation of this system and evaluate this system with data set collected for movement analysis study. The broader aim of this paper is to present a solutions space achievable based on medical imaging informatics design and methods for improvement in workflow for human performance analysis in a biomechanics research lab.
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Sneha Verma, Sneha Verma, Jill McNitt-Gray, Jill McNitt-Gray, Brent J. Liu, Brent J. Liu, } "Medical imaging informatics based solutions for human performance analytics", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105791R (6 March 2018); doi: 10.1117/12.2297663; https://doi.org/10.1117/12.2297663

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