Microsoft Research
Computational User Experiences

Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises

Although numerous devices exist to track and share exercise routines based on running and walking, these devices offer limited functionality for strength-training exercises. We introduce a system for automatically tracking repetitive exercises – such as weight training and calisthenics – via an arm-worn inertial sensor. Our goal is to provide real-time and post-workout feedback, with no user-specific training and no intervention during a workout. Toward this end, we address three challenges:

(1) Segmenting exercise from intermittent non-exercise periods
(2) Recognizing which exercise is being performed
(3) Counting repetitions

We present cross-validation results on our training data and results from a study assessing the final system, totaling 114 participants over 146 sessions. We achieve precision and recall greater than 95% in identifying exercise periods, recognition of 99%, 98%, and 96% on circuits of 4, 7, and 13 exercises respectively, and counting that is accurate to ±1 repetition 93% of the time. These results suggest that our approach enables a new category of fitness tracking devices.

The automatic counting portion of this work shipped as part of the Microsoft Band.



Dan Morris


Scott Saponas


Andrew Guillory


Ilya Kelner




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Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises

Dan Morris, T. Scott Saponas, Andrew Guillory, Ilya Kelner

Proceedings of ACM CHI 2014, April 2014


Contact Dan Morris and Scott Saponas for questions about our work in this area.

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