Publication
Passive Capacitive based Approach for Full Body Gym Workout Recognition and Counting
Sizhen Bian; Vitor Fortes Rey; Peter Hevesi; Paul Lukowicz
In: IEEE International Conference on Pervasive Computing and Communications. IEEE International Conference on Pervasive Computing and Communications (PerCom-2019), 17th, March 11-15, Kyoto, Japan, Pages 136-145, ISBN 978-1-5386-9148-9, IEEE, 4/2019.
Abstract
In this work, we present the design and implementation of a micro watt level power consumption, human body capacitance based sensor for recognizing and counting gym workouts. The concept also works when the device is attached to a body part which is not directly involved in the activity's movement. In contrast, most of the widely used motion sensing based approaches require placing the sensor on the moving body part (e.g. for analyzing leg based gym exercises the sensor needs to be placed on the leg). We described the physical principle behind the ubiquitous electric coupling between human body and environment, and explored the capability of this sensing modality in gym workouts. We evaluated our sensor with 11 subjects, performing 7 popular gym workouts each day over 5 days with our sensor being placed at 3 different body positions, including a non-contact position, where the sensor is placed in the subject's pocket. Results showed that our sensing approach achieved an average counting accuracy of 91\%, which is highly competitive with commercial devices on the market. The mean leave one user out workout recognition f-scores obtained were of 63\%, 56\%, 45\% for sensors located on wrist, on calf and in pocket, respectively. As every subject performed activities over multiple days changing shoe height, shoe and clothes type, we demonstrate that full body activity counting and to some extent recognition is feasible, regardless of personal habit of movement speed and scale.