Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Low-cost wireless surface EMG sensor using the MSP430 microcontroller

Beneteau, Armand and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Soraghan, John (2014) Low-cost wireless surface EMG sensor using the MSP430 microcontroller. In: 6th European Embedded Design in Education and Research Conference (EDERC), Milano, Italy, 2014. IEEE, Piscataway, NJ, pp. 264-268. ISBN 9781479968411

[img]
Preview
PDF (Beneteau-etal-IEEE-2014-Low-cost-wireless-surface-EMG-sensor)
Beneteau_etal_IEEE_2014_Low_cost_wireless_surface_EMG_sensor.pdf - Preprint

Download (982kB) | Preview

Abstract

Electromyography is the study of the voltage polarisation signals generated during body muscle contractions. Surface EMG is non-invasive and is ideal for applications such as training, rehabilitation and active prosthesis control. Despite the progress in technology, myoelectric prostheses currently on the market still adopt simple analog sensors. Digital EMG acquisition is now common practice in research and academic institutions, but it involves fairly expensive pieces of equipment. This paper describes the implementation of a low-cost wireless surface EMG digital sensor based on the MSP430 microcontroller from Texas Instruments. The proposed acquisition system fully exploits the capabilities of the eZ430-RF2500 Development Tool to digitise surface EMG signals, transmit them wirelessly between nodes and finally read them into a PC for further processing. Experimental results demonstrate the effectiveness of the proposed system in acquiring sEMG signals for pattern recognition and real-time control.