Measurement of Human Performance

“If I had an hour to solve a problem and my life depended on it, I would use the first 55 minutes determining the proper question to ask.” -Albert Einstein


We are currently living in a world where a lot of data is being captured on nearly all human endeavors, and with such detail, that our ability to extract valuable information from this data is often overwhelmed. By the year 2025, it is estimated that the amount of data worldwide will increase 10-fold, with a rate of data creation of 163 zettabytes (1 zettabyte = 1 trillion gigabytes) per year. But all of this data is of no value to us if we don’t use it in some way other than occupier of hard drive storage. Data needs to be processed, analyzed, and interpreted in order for it to transition from potential value to positively impacting our lives. Given the vast and ever-increasing sea of data, there is an overwhelming need for individuals with the skills to manage, explore, analyze, interpret, and communicate this data.

Research in the human performance domain is by no means exempt from the “data revolution,” but the traditional process of obtaining empirical evidence is slow, costly, and often limited in real-world applicability, such as with laboratory-based experimentation. As Robert Pirsig writes, “the traditional scientific method has always been at the very best, 20-20 hindsight. It’s good for seeing where you’ve been. It’s good for testing the truth of what you think you know, but it can’t tell you where you ought to go, unless where you ought to go is a continuation of where you were going in the past.” The premise of this course is that data science, an emerging field of inquiry, has the potential to form the missing link between research-derived knowledge about the past and data-driven prescription to improve human performance in the future.

This course will provide students with experience in framing a research question and seeking out appropriate publically available data to answer that question. Students will learn how to approach a data set, how to perform an initial exploration of the data, how to conduct simple statistical analyses to test one or more hypotheses relevant to the data, and how to interpret and present the results of their investigation. Emphasis will be on the practical aspects of data science (as opposed to the theoretical/statistical aspect). Practical experience will be supplemented with a foundational understanding of valid and reliable measurement in the domain of kinesiology, with emphasis on those currently deployed in wearable technologies.


Physiological measurement
Heart rate
Blood oxygen saturation
Ventilatory flows
Indirect calorimetry
Expired gas composition
Data capture techniques and tools
Measurement theory
Basic electronics
Instrument calibration
Analog to digital conversion
Computational data techniques
Signal processing
Research communication
Digital notebooks
Website development
Interactive graphics