Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Sampling rate impact on energy consumption of biomedical signal processing systems
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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, 912 June 2015 Piscataway, NJ: IEEE, 2015 ISBN: 9781467372022 ISBN: 9781467372015 S.254259 
 International Conference on Wearable and Implantable Body Sensor Networks (BSN) <12, 2015, Cambridge/Mass.> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IIS () 
Abstract
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 BachmannLandau notation has been used to evaluate the computational complexity in dependency of the sampling rate. We found linear, logarithmic, quadratic and cubic dependencies.