Conference contribution


Real-Time ECG and EMG Analysis for Biking Using Android-Based Mobile Devices


Publication Details
Author(s): Richer R, Blank P, Schuldhaus D, Eskofier B
Title edited volumes: Proceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014
Publisher: IEEE Computer Society
Publication year: 2014
Conference Proceedings Title: Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on
Pages range: 104-108

Event details
Event: 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Event location: Zürich, Switzerland
Start date of the event: 16/06/2014
End date of the event: 19/06/2014

Abstract

We developed an application for Android-based mobile devices that enables a real-time calculation of heart rate and cadence for biking. Therefore, both ECG and EMG data are acquired in real time by Shimmer sensors and transmitted via Bluetooth, as well as processed and evaluated on the mobile device. The ECG algorithm is based on the Pan-Tompkins algorithm for QRS-Detection and offers a heart beat detection rate of more than 94%. The EMG algorithm offers a treadle detection rate of more than 91%. The application's range of features is complemented by GPS data for the calculation of speed and location information. It is available for download and can for example be used for controlling the user's training status, for live training supervision and for the subsequent analysis of the various training runs. © 2014 IEEE.



How to cite
APA: Richer, R., Blank, P., Schuldhaus, D., & Eskofier, B. (2014). Real-Time ECG and EMG Analysis for Biking Using Android-Based Mobile Devices. In Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on (pp. 104-108). Zürich, Switzerland, CH: IEEE Computer Society.

MLA: Richer, Robert, et al. "Real-Time ECG and EMG Analysis for Biking Using Android-Based Mobile Devices." Proceedings of the 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Zürich, Switzerland IEEE Computer Society, 2014. 104-108.

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