Another study utilized a combination of high bandwidth kinetic (force plate) and low bandwidth kinematic (motion capture) data collected on professional basketball players to develop unique clusters based on their dynamic movement strategy (2). The ability to jump high is no doubt a common thread among basketball players, however the movement strategies utilized can vary greatly. For example, different individuals may be categorized as being either knee-dominant or hip-dominant while they jump, yet this strategy may not significantly impact jump height! Clustering analyses allow us to take an objective approach to identify what unique groupings of movement strategies exist.
Ultimately the goal of all of this analysis and data science is to learn something meaningful that we can implement or utilize to better improve our outcomes and processes. Learning about how we can categorize different movement strategies of NBA players is no doubt interesting, but how does that information help us?
Learning from data
We explained earlier that even without the labeled data for which credit card purchases are fraudulent, it is still possible to flag suspicious purchases by simply monitoring the behavior of credit card users and identifying when behavior becomes ‘atypical.’ The flag can potentially trigger real-time notifications or interventions, for example calling or text messaging the cardholder. This simple action can not only improve outcomes by catching fraudulent purchases earlier, but also generates additional labeled data from the cardholder (confirmation of the purchase in question) for future utility.
Some research suggests that while vertical jump height is a measure that can be utilized to identify neuromuscular fatigue, monitoring changes to the underlying jump strategy may actually be more useful (3,4). Throughout our years of collecting force plate data on tens of thousands of individuals we have noticed that most healthy individuals have a relatively consistent movement strategy, or what we would call Movement Signature. So when an individual suddenly alters their movement strategy, this is definitely something worth investigating: perhaps fatigue is present, or pain or soreness at a specific joint… Underlying mental, emotional, or social stressors may significantly be impacting motivation or sleep…
This can alert clinicians when some sort of movement compensation is occurring that is atypical for that individual. This type of insight is well suited for the clustering techniques we’ve just discussed: we can automatically flag an individual after assessing if their cluster has changed, indicating a significant change from their ‘norm’ or baseline Movement Signature.
At Sparta Science, we have countless stories of practitioners uncovering meaningful information such as joint pain, academic-related stress, and even signs of eating disorders by consistently and objectively assessing movement strategies and monitoring for significant deviations from typical results or behavior. This objective information arms practitioners with the evidence to support their intuition and ultimately guide their interventions to achieve the best outcome.
Big Picture Takeaways:
- Traditional statistical approaches were developed on small datasets, and while still useful, “big data” approaches should be leveraged on the growing number of large, complex datasets.
- Unsupervised learning provides a data-driven approach to uncovering hidden patterns or groupings from human movement and physiological data that are often assumed intuitively.
- The learnings that can be achieved with a “big data” approach can help to guide and support experts on decision-making and best practices for years to come.
References:
- Shelly, Zachary, et al. “Using K-means clustering to create training groups for elite American football student-athletes based on game demands.” International Journal of Kinesiology and Sports Science 8.2 (2020): 47-63.
- Rauch, Jacob, et al. “Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players.” International Journal of Environmental Research and Public Health 17.17 (2020): 6394.
- Legg, Jan, et al. “Variability of jump kinetics related to training load in elite female basketball.” Sports 5.4 (2017): 85.
- Gathercole, Rob, et al. “Alternative countermovement-jump analysis to quantify acute neuromuscular fatigue.” International journal of sports physiology and performance 10.1 (2015): 84-92.