Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BSN.2015.7299392 | 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) |
Keywords | Field | DocType |
sampling rate,energy consumption,biomedical signal processing systems,battery runtime,wearable sensor systems,power consumption,mobile sensors,biomedical sensor applications,signal quality,data rate,Bachmann-Landau notation,computational complexity,linear dependency,logarithmic dependency,quadratic dependency,cubic dependency | Mobile computing,Signal processing,Computer science,Wearable computer,Sampling (signal processing),Quadratic equation,Real-time computing,Logarithm,Energy consumption,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2376-8886 | 4 | 0.45 |
References | Authors | |
4 | 13 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Tobola | 1 | 8 | 2.74 |
| Franz J. Streit | 2 | 8 | 1.05 |
| Chris Espig | 3 | 8 | 1.05 |
| Oliver Korpok | 4 | 8 | 1.05 |
| Christian Sauter | 5 | 4 | 0.45 |
| Nadine Lang | 6 | 4 | 2.48 |
| Bjorn Schmitz | 7 | 8 | 1.05 |
| C. Hofmann | 8 | 36 | 6.45 |
| Matthias Struck | 9 | 21 | 3.20 |
| Christian Weigand | 10 | 9 | 3.12 |
| Heike Leutheuser | 11 | 22 | 5.19 |
| Björn M. Eskofier | 12 | 38 | 6.97 |
| georg fischer | 13 | 51 | 24.65 |