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2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2016)
Sydney, Australia
March 14, 2016 to March 19, 2016
ISBN: 978-1-4673-8778-1
pp: 1-9
Bo Zhou , German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Trippstadter Straße 122, 67663, Kaiserslautern, Germany
Mathias Sundholm , German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Trippstadter Straße 122, 67663, Kaiserslautern, Germany
Jingyuan Cheng , German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Trippstadter Straße 122, 67663, Kaiserslautern, Germany
Heber Cruz , German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Trippstadter Straße 122, 67663, Kaiserslautern, Germany
Paul Lukowicz , German Research Center for Artificial Intelligence (DFKI) and University of Kaiserslautern, Trippstadter Straße 122, 67663, Kaiserslautern, Germany
ABSTRACT
We present a wearable textile sensor system for monitoring muscle activity, leveraging surface pressure changes between the skin and an elastic sport support band. The sensor is based on an 8×16 element fabric resistive pressure sensing matrix of 1cm spatial resolution, which can be read out with 50fps refresh rate. We evaluate the system by monitoring leg muscles during leg workouts in a gym out of the lab. The sensor covers the lower part of quadriceps of the user. The shape and movement of the two major muscles (vastus lateralis and medialis) are visible from the data during various exercises. The system registers the activity of the user for every second, including which machine he/she is using, walking, relaxing and adjusting the machines; it also counts the repetitions from each set and evaluate the force consistency which is related to the workout quality. 6 people participated in the experiment of overall 24 leg workout sessions. Each session includes cross-trainer warm-up and cool-down, 3 different leg machines, 4 sets on each machine. Plus relaxing, adjusting machines, and walking, we perform activity recognition and quality evaluation through 2-dimensional mapping and the time sequence of the average force. We have reached 81.7% average recognition accuracy on a 2s sliding window basis, 93.3% on an event basis, and 85.6% spotting F1-score. We further demonstrate how to evaluate the workout quality through counting, force pattern variation and consistency.
INDEX TERMS
Muscles, Robot sensing systems, Force, Electrodes, Biomedical monitoring, Monitoring, Hardware
CITATION

B. Zhou, M. Sundholm, J. Cheng, H. Cruz and P. Lukowicz, "Never skip leg day: A novel wearable approach to monitoring gym leg exercises," 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), Sydney, Australia, 2016, pp. 1-9.
doi:10.1109/PERCOM.2016.7456520
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