With the resurgence of force pates in recent years, their use and the data they provide are more accessible than ever before due to the commoditization of the hardware. What used to cost tens of thousands of dollars can now be manufactured and sold for a few hundred.
For the sake of this post, we will reference the data that is available in the Sparta Movement Health Platform. Keep in mind that Sparta Science provides a fully comprehensive data model and machine learning ecosystem. Four levels of data are available to Sparta customers: Sparta Metrics, Normalized Data, Raw Data, and Time-Series Data. The purpose of this is not self-promotion but rather a disclaimer that not all force plate software providers can collect, store and calculate data force plate data the way Sparta Science does.
Always Remember Step 1: Standardization
First, movement data is taken from its source (forces) and standardized with built-in error-detection and reliability software automation. Sparta Science is the only solution that provides this objective filtering as standard, which means the data and insights can be trusted and scaled.
From there, Sparta can provide the trusted data required and the ability to leverage machine learning and artificial intelligence capabilities. The four types of data and their utility and accessibility can be seen in the table below.
The Four Types of Data
Data For Action, Now
First and foremost, Sparta Science is driven by providing the most reliable, valid, and actionable metrics that facilitate real-time decision making in operational settings. This is where the Sparta Metrics and Normalized Data are best placed to provide instant value and engagement because they can easily be communicated and improved through targeted interventions. An example is that, in 60-seconds, an individual can understand their movement vulnerabilities and performance strengths compared to their peers via their Injury Risk and Sparta Scores. At the same time, this information can be shared with multiple clinical and management stakeholders for aligned awareness and decision making.
Data For The Learning, Later
While real-time actionability should always be the end goal, the option to interact with data for a range of research, education, and customized problem-solving needs can also be an important capability. This is where the Time-Series and Raw Data are valuable. What should be noted, however, is that these types of data generally require researchers and applied scientists to interpret, analyze and communicate meaning. While important for learning and innovation, this data is less actionable and engaging to all stakeholders and can oftentimes be a distraction to practitioners needing to make real-time decisions. An example could be exporting a large, longitudinal dataset and sharing this with a research and development partner to analyze and explore a range of bespoke questions.
Sparta is constantly reviewing millions of data points at all levels of its data model, both internally to refine methods and strategically alongside its industry and research and development partners. Leveraging its aggregated global database and a constantly evolving feedback loop of machine learning techniques, Sparta continues to search and explore new data science approaches and metrics to improve movement health and performance outcomes.