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This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications.
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Two examples of classes of fusion systems formalized in the paper are data fusion and decision fusion; decision fusion is shown to be a subclass of data fusion.
The databases were searched using the two types of data fusion: similarity fusion (SF) and group fusion (GF).
Furthermore, they did not address the issue of data fusion.
E agr is the energy consumption of data fusion.
This idea of data fusion is also pursued by C.
A recent overview of data from 20 European countries showed that, internationally, chiropractors continue to treat children.
Then, Section 3 presents an overview of data aggregation.
An overview of data on overall survival is presented in Table 2.
Table 4 gives an overview of data collection.
For overview of data collection, see table 1.
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