What is "Machine Learning" and how does Sparta Science utilize it?

This article discusses some of the foundational components of how Machine Learning is leveraged as part of the Movement Health Platform.


Machine Learning (ML), Data Science, and Artificial Intelligence (AI) are commonplace terms used in the marketing of products and services - but what do they mean, and how do they relate to the Movement Health Platform?

Though these terms see frequent use, they defy precise definitions. Depending on who is talking, in what context, and when, any one of these terms might be used to describe a particular technology use case. The common thread is that all three terms concern the use of computational tools to process and extract meaning from data. The Venn diagram below captures a common (though by no means universal) opinion on how these concepts relate to each other.

AI-ML Venn Diagram

What is machine learning?

An Internet search will yield a vibrant discussion of these three subject areas, but for our purposes, the following synopsis is deemed sufficient:

  • Artificial Intelligence: the use of computers to emulate human intelligence capabilities
  • Machine Learning: the use of computation to automatically learn from experience captured in data
  • Data Science: a set of tools used to process, describe, and transform data in order to extract meaning

In describing what we do at Sparta Science, we most frequently use Machine Learning terminology, because the software tools and algorithms we use are most frequently characterized in that way. These tools include the use of neural networks (referred to as Deep Learning) in addition to several others.

Why is ML needed?

Artificial intelligence and machine learning tools have existed for decades, but the use of machine learning has grown exponentially in recent years. This is largely due to a combination of exponential improvements in computer processing capabilities and an exponential increase in the amount of data being generated. We now have more data than ever, but luckily we also have more tools and computing power to analyze that data.

The key reason to utilize machine learning is to leverage computers to process the vast amount of quantity of data that now exists. For example, force plate sensors collect 1000 data points per second from four different sensors. A single 2-minute assessment would provide 480,000 data points. Now multiply that by 100 individuals performing the assessment... and we can quickly see the need to leverage powerful computers to store, process, and analyze the 48 million data points we’ve generated into reliable and useful information. This "big data" approach has been successfully applied to a variety of industries, such as eCommerce, and is just beginning to be utilized in health.

How does Sparta Science use Machine Learning?

The specific terminology matters far less than what we do. In the case of the Movement Health Platform (MHP), there are four primary components of our use of machine learning. 
  1. Feature Extraction or Learning: transforms high-frequency time series data into differentiating features (or biomarkers) that characterize a person’s movement health at a particular time
  2. Prediction Models: take sets of features (or biomarkers) as inputs and transform them into output assessments (e.g., risk or performance capabilities) or recommendations (e.g., activity programs or things to avoid)
  3. Dimension Reduction: produce reduced sets of independent biomarkers from base sets that are highly reliable utilizing a combination of optimization and component analysis techniques
  4. Cluster Analysis: identify groupings and biomarker patterns within populations of individuals utilizing unsupervised learning techniques (e.g., movement strategies)

Feature Learning

When a user executes a single scan test such as a Balance or Jump Scan, the Sparta Scan Kit collects on the order of 1 million measurements. The goal of Feature Learning is to transform those million measurements into a form that captures the essential pattern or signature in that user’s movement.

In the Movement Health context, there are two approaches to extracting features from the time series data:

  • Physics-driven: Use biomechanics concepts to identify specific characteristics of force-time series data that could be useful features. Then, use numerical analysis to calculate these features from the time series data.
  • Data-driven: Use machine learning techniques to extract features (or biomarkers) directly from the 1000 Hz force-time series data through pattern recognition

Prediction Models

One of the key components of machine learning is to use incoming data to predict the likelihood of a future outcome. It is a prerequisite to have large amounts of data to ensure accurate predictions can be made. The Movement Health Platform uses extracted features (that we commonly refer to as biomarkers in our application) to build and use Prediction Models. These models are built using supervised and semi-supervised learning techniques that combine feature data with injury, performance, and activity data. These models aim to transform the biomarkers extracted from scan results into assessments of risk (e.g., relative injury risk, fall risk) and performance, as well as recommendations that guide users' actions to improve their movement health.

Dimension Reduction

Through featuring learning, biomarkers are extracted from high-frequency time-series data which are further reduced and optimized. Component analysis techniques are utilized to decrease biomarker redundancy and identify primary biomarkers that are independent (e.g., represent different movement capabilities or functions) and highly reliable (i.e., results are consistent from test to test). The aim of dimension reduction is to produce biomarkers that provide utility (i.e., are reliable and meaningful measures) in clinical and operational environments.

Cluster Analysis

Unsupervised learning techniques can be leveraged for many different types of clustering methods, which group objects (or individuals) by similarity based on one or more specific features or biomarkers. Clustering can enable the identification of unique groups and subgroups of individuals within populations based on the complex interaction of movement health biomarkers.

Other Benefits of ML

Accuracy and Reliability of Data Collection

Another critical use of ML in the context of feature learning is ensuring the collected data's integrity or quality. Though the scan tests are relatively simple and force plates are generally reliable, things can go wrong in ways that can invalidate the data, e.g.:

  • Users can incorrectly execute a scan test - step off the plate prematurely, remove clothing mid-test, fail to follow an instruction
  • Hardware/setup issues - uneven surfaces where a foot is off the ground, weight calibration issues, potential sensor errors
Sparta Science uses ML techniques to validate collected data, automatically identify issues that could jeopardize data integrity, and guide corrective action.

Efficiency, Simplicity, and Practicality

Machine-learning models are utilized consistently in our Product Research and Development stages to ensure that the data collection process is practical and quick and that the visualization and interpretation of data are simple and intuitive. For example, different unsupervised clustering models are utilized to help identify natural groupings of data as well as data redundancies that allow our Movement Health Platform and Customer Success team to surface only the most critical information. The increase in data collection capabilities can be overwhelming, leading to paralysis analysis because of the volume of data that exists. Our modeling allows the ability to surface the minimum set of meaningful data points and references that can provide insight and information and drive intervention or action. Additionally, these models are deployed directly into our software and run in real time on our devices. This enables there to be zero lag time from data collection, through data analysis, and into data interpretation.

Continuous Future Improvement

The Sparta Science approach facilitates continuous improvement and expansion of ML-based capabilities over time as more data is collected. The Movement Health Platform allows for frequent updates and the addition of models over time. An update of models occurs when the platform determines that new models that incorporate more recent data outperform their predecessors (relevant to both Feature Learning and Prediction models). The addition of models can take several forms, including:

  • New feature/biomarker discovery
  • Expanded Prediction Model functional granularity:
  • Injury type-specific models
  • Performance characteristic-specific models
  • Activity-type specific recommendations
  • Population/Group-specific models: As sufficient quantities of data is collected for specific groups, the possibility exists to create models specific to that group that may provide additional insights.
  • Longitudinal Prediction Models: As data is collected over time for individuals, the identification of patterns of change over time frames on the order of weeks becomes possible.

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