Not a side activity. Every Fitness Tech device starts as a research question — we find a gap in what existing fitness technology can measure, then build the hardware that closes it.
That ordering is deliberate. We don't build a gadget and then look for something to study. The Research team defines the question before the Hardware team builds the answer — and validation against commercial and medical-grade equipment is part of the build, not an afterthought.
Members get hands-on experience with literature review, testing protocol design, data collection, statistical analysis, and model training.
Each question maps to a device in the portfolio — the question came first
How accurately can student-built sensors capture biometrics compared to commercial and medical-grade devices?
Can motion data from a wrist-worn IMU reliably classify exercise type and count reps across different users and different form quality?
What sampling rate and sensor configuration is the minimum viable one for trustworthy rep detection?
How much does wearability and comfort affect adherence over a multi-week training block?
How does hydration — measured automatically rather than self-reported — actually correlate with training performance and recovery markers?
Can a load cell and flow sensor combination achieve volume accuracy sufficient for research use in a consumer form factor?
Does adaptive hydration targeting improve adherence compared with a fixed daily goal?
What does a coach need to see, and at what frequency, to make better roster decisions from live athlete data?
Which combination of signals gives the earliest useful warning that an athlete is trending toward overtraining or injury?
Commercial devices are black boxes. You can't see how the data is processed, can't customize what's tracked, and can't use them to answer new questions. Because we design the sensor platform, the firmware, and the app that receives the stream, the entire pipeline stays inspectable — which is the difference between a study we can defend and a number we have to take on faith.
Building a portfolio of validated, publishable work that future cohorts inherit and extend
We're working to formalize faculty partnerships and UW lab collaborations for mentorship and academic guidance.
No prior research experience required — the team teaches the method as it runs the studies
Survey existing studies on wearable accuracy, hydration science, and training-load monitoring to find the gap worth building for
Design the testing procedure: what gets measured, against what reference, under what conditions, with how many subjects
Run the discovery conversations that decide which device we build next and what the coach dashboard has to show
Run user testing sessions with our own hardware, and build the labelled datasets that train our classification models
Process and analyze collected data with statistical methods and visualization tools; benchmark against reference devices
Write research reports, build presentations, and prepare symposium and conference submissions
Join the team that decides what we build next — and gets to find out whether it works.
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