Publikation

Smart-Mat: Recognizing and Counting Gym Exercises with Low-cost Resistive Pressure Sensing Matrix

Mathias Sundholm, Jingyuan Cheng, Bo Zhou, Akash Sethi, Paul Lukowicz

In: (Hrsg.). International Conference on Ubiquitous Computing (Ubicomp-14) ACM 2014.

Abstrakt

There is a large class of routine physical exercises that are performed on the ground, often on dedicated ”mats” (e.g. pushups, crunches, bridge). Such exercises involve coordinated motions of different body parts and are difficult to recognize with a single body worn motion sensors (like a step counter). Instead a network of sensors on different body parts would be needed, which is not always practicable. As an alternative we describe a cheap, simple textile pressure sensor matrix that can be unobtrusively integrated into exercise mats to recognize and count such exercises. We evaluate the system on a set of 10 standard exercises. In an experiment with 7 subjects, each repeating each exercise 40 times, we achieve a recognition rate of 89% and a counting accuracy of 87%. The paper describes the sensor system, the recognition methods and the experimental results.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence