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Sampling rate impact on energy consumption of biomedical signal processing systems

: Tobola, A.; Streit, F.J.; Espig, C.; Korpok, O.; Sauter, C.; Lang, N.; Schmitz, B.; Hofmann, C.; Struck, M.; Weigand, C.; Leutheuser, H.; Eskofier, B.M.; Fischer, G.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 : Cambridge, Massachusetts, USA, 9-12 June 2015
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-7202-2
ISBN: 978-1-4673-7201-5
International Conference on Wearable and Implantable Body Sensor Networks (BSN) <12, 2015, Cambridge/Mass.>
Conference Paper
Fraunhofer IIS ()

Long battery runtime is one of the most wanted properties of wearable sensor systems. The sampling rate has an high impact on the power consumption. However, defining a sufficient sampling rate, especially for cutting edge mobile sensors is difficult. Often, a high sampling rate, up to four times higher than necessary, is chosen as a precaution. Especially for biomedical sensor applications many contradictory recommendations exist, how to select the appropriate sample rate. They all are motivated from one point of view - the signal quality. In this paper we motivate to keep the sampling rate as low as possible. Therefore we reviewed common algorithms for biomedical signal processing. For each algorithm the number of operations depending on the data rate has been estimated. The Bachmann-Landau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.