KEYWORDS: Data processing, Databases, Biomedical applications, Data storage, Sports medicine, Biomechanics, Video, Information science, Data modeling, System integration
Integration of sports medicine and human performance is a developing field with the potential to aid people of various abilities. Previous work done has shown the normalization of sports medicine data as well as the building of a framework for the storage and retrieval of sports medicine data. The result of this is the Integrated Biomechanics Informatics System (IBIS). IBIS has the capacity to store, view, and retrieve data and can also be expanded to include different sports medicine research related applications. One such application is a data processing application to create force vector overlays for decision support. Users were able to log into IBIS using secure personal logins and access the data processing application to create force vector overlays. The efficacy of the data processing application was tested by having users process data to create force vector overlays using the IBIS application and then comparing it to the traditional workflow. The new workflow using the IBIS application was found to be quicker and easier to use than the traditional workflow while generating identical results. The deployment of this data processing tool for decision support shows the capability of IBIS to be expanded to include more tools such as automatic foot contact detection. In the future we hope to apply these developed tools in a clinical setting with our collaborators at Rancho Los Amigos National Rehabilitation Center, and therefore show the efficacy of this tool and IBIS in a clinical environment with a variety of patients.
KEYWORDS: Data processing, Databases, Biomechanics, Video, Data modeling, Sports medicine, Decision support systems, System integration, Computing systems, Information science
Sports medicine research is a developing field which uses a processed combination of videos, force data, and accelerometer data. In radiology data is processed within the radiology workflow before being stored in PACS. Both processed and raw data can be uploaded to a central database. Currently, in the biomechanics workflow this data must be processed by researchers on local computers. There is currently no centralized method to process this data, each researcher must use unique scripts which require specific computer environments. Also, once data has been stored in a central database there is no method to query that data for further processing. Processed data is a valuable tool for researchers to educate athletes and coaches. Therefore, there is a need for a central, standardized system which can process data within the biomechanics workflow. The integrated biomechanics informatics system (IBIS) is an ideal choice for this system as it already provides a centralized database used by biomechanics researchers. Using an imaging-informatics approach we have developed a web application which allows users to query, retrieve, and process data on the IBIS system. The web application’s efficacy will be measured by testing expert and novice users on the speed and ease of using the web application and comparing the results of web processing to local processing. In the future this web application can be extended to host multiple types of biomechanics data processing on the IBIS system.
KEYWORDS: Data storage, Databases, Sports medicine, System integration, Information science, Imaging systems, Data modeling, Data integration, Video, Standards development
Sports medicine research is a developing field which uses a processed combination of videos, force data, and accelerometer data. Currently raw data is transferred onto hard drives and then manually processed by researchers. However, there is currently no standard method to store the raw, unprocessed data collected by the various sports sensors. These sensors lack the capability to store raw data themselves, so data must be stored elsewhere after collection. Once this data is stored there is no streamlined way to tag or retrieve this data. Sports data is also often shared between collaborating institutions. Institutions need a way to centrally store data to facilitate easier transfer between institutions. In a similar fashion, imaging modalities acquire raw imaging data that is currently stored directly in the modality systems. Therefore, there is a need for a standardized, streamlined system which can both store the raw, unprocessed data and can tag that raw data to allow for more efficient data query. Using an imaging-informatics based approach we have developed a “will-call” data mart which allows users to store raw sports data along with tags. This data mart will allow users to quickly query and retrieve data. The data mart’s efficacy will be measured by testing expert and novice users on the speed and ease of using the data mart and comparing it to the traditional method of data storage. In the future this data mart can be integrated into other sports related systems such as the integrated biomechanics informatics system (IBIS).
The field of biomechanics integrates a variety of data types to provide meaningful feedback to athletes, coaches, and healthcare professionals to reduce injury and improve performance. These different sources of data include waveform, video, discrete, and performance metrics. An integrated biomechanics informatics system (IBIS)1,2 has been developed to apply imaging informatics principles to the field of biomechanics. The IBIS system helps create a space for stakeholders to make informed decisions using multimodal biomechanics data. This current work details the development of two new decision support tools within the IBIS system that enhance the ability of users to effectively analyze and interpret data. These two tools include an R Shiny statistical analysis tool and an algorithm for detecting foot contact with the ground. Organizing data into SQL databases on the IBIS system provides the backend for the R Shiny analysis tools. This connection provides statistical analysis tools to review data over time and across individuals. Its integration also incorporates interactive data visualization techniques to put biomechanical data in context. A semi-automated foot contact detection algorithm was created to reduce the need for researchers to manually search through thousands of video frames to synchronize video data accurately with other information such as force data. Overall, these two improvements to the system will support researchers working to provide meaningful insight about human performance and injury prevention. These additions to the IBIS system provide quicker access to data, develop standardized pipelines, and create additional time for biomechanists to focus on interpreting results rather than data management.
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