Is there current research into the implemention of Randomness Extractors?
Much of extractor literature is about minimizing the seed length, which is important for derandomization application. However, it may not be crucial for yours. Also, often the literature focuses on relatively large error (e.g., 1/100), which is fine for derandomization but may be problematic in other settings, that require an exponentially small error.

In your setting, it may be OK to generate once and for all a long random seed (say by tossing coins), and then use it to extract. In this case you could use pairwise independent hash functions that have rather efficient implementations. I wrote a paper with Shaltiel and Tromer on this issue. You may also be able to use almost independent hash functions, that can be more efficient and have smaller seed. (Don't know offhand a good reference for their efficient implementation, though there have been several works on this.)

If you have multiple sources that are independent, then you can do better things. The classical Hadamard extractor works if the entropy rate is larger than 50% (this should be mentioned in the surveys above). If the entropy is smaller than 50% then we had one simple construction with Impagliazzo and Wigderson . The dependence between of number of sources and error achieved on the entropy rate is not ideal, though to really understand it you'll need to look at the exact bounds given by today's state of the art sum product theorems. (And if you're willing to assume certain number theoretic conjectures you can get even more efficient extractors.) This construction has been greatly improved in various ways, some of which could be relevant to your application. A great source for these is Anup Rao's Thesis.