Commit 4268209c authored by burcharr's avatar burcharr 💬
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......@@ -72,11 +72,11 @@ The external data sets used are:
- REALDISP @banos_benchmark_2012
- PAMAP2 @reiss_introducing_2012
The external data sets were collected and converted by Daniel Homm, analyzed and resampled by us. Their contents can be seen in table \ref{tbl:datasets}.
The external data sets were collected and converted by Daniel Homm, analyzed and resampled by us. Their contents can be seen in table \ref{tbl:datasets}. They mainly contain activities which involve a lot of movement, which we expect to be helpful in avoiding false positives, as explained above.
### Specifications of the resulting data set used
The final data set used contains a total of 14.4 million 6-dimensional data points. With these 14.4 million data points we created windows of length 150 samples (3 seconds), with 50% overlap. This left us with ~194,000 windows. Out of those windows, ~15,750 ($8,2\,\%$) contained hand washing, ~178,500 ($91,8\,\%$) were other activities or idle. Out of the ~15,750 hand washing windows, ~10,250 ($65\,\%$) were compulsive hand washing windows, ~5500 ($35\,\%$) were non compulsive washing. We note that for most machine learning methods it makes sense to balance the training set with regard to the classes, in order to avoid biases towards the more frequent classes. In specific machine learning algorithms, one could also combat the class imbalance problem using an importance weighting for the different classes. To train a neural network, the loss function can also be weighted by the class frequency.
The final data set used in this thesis contains a total of 14.4 million 6-dimensional data points. With these 14.4 million data points we created windows of length 150 samples (3 seconds), with 50% overlap. This left us with ~194,000 windows. Out of those windows, ~15,750 ($8,2\,\%$) contained hand washing, ~178,500 ($91,8\,\%$) were other activities or idle. Out of the ~15,750 hand washing windows, ~10,250 ($65\,\%$) were compulsive hand washing windows, ~5500 ($35\,\%$) were non compulsive washing. We note that for most machine learning methods it makes sense to balance the training set with regard to the classes, in order to avoid biases towards the more frequent classes. In specific machine learning algorithms, one could also combat the class imbalance problem using an importance weighting for the different classes. To train a neural network, the loss function can also be weighted by the class frequency.
## Description of different classification problems
\label{sec:classification_problems}
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