RecoFit: Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises
- Dan Morris ,
- Scott Saponas ,
- Andrew Guillory ,
- Ilya Kelner
CHI '14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems |
Published by ACM
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 RecoFit, 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, and (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.
论文与出版物下载
Exercise Recognition from Wearable Sensors
17 6 月, 2019
This data set contains accelerometer and gyroscope recordings from over 200 participants performing various gym exercises. This data set is described in more detail in the associated manuscript: Morris, D., Saponas, T. S., Guillory, A., & Kelner, I. (2014, April). RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3225-3234). ACM.