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Merge branch 'master' of https://git.frank-ebner.de/FHWS/IPIN2018

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toni
2018-10-21 14:42:53 +02:00
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@@ -2,7 +2,7 @@
Dear Reviewer,
-> At first we would like to start with a short overview over all changes. The individual answers follow directly after this text. All additions to the text are highlighted in blue. Words or text passages suggested by the reviewers to be removed are highlighted in red. As you will see, abstract and introduction are completely revised to better highlight the novel contributions. We were able to implement many suggestions of the reviewers. The transition was highly extended, to achieve a better understanding of the method. We have also added a detailed description of how and by what means our system is installed in a building. This also leads to a better description of the experimental setup.
We added a complete new section, evaluating the activity recognition. Additionally, you will find mind smaller changes and addition throughout the paper as well as further improvements of the writing. In the following our answers are marked with "->".
We added a complete new section, evaluating the activity recognition. Additionally, you will find many smaller changes and addition throughout the paper as well as further improvements of the writing. In the following our answers are marked with "->".
The paper presents an improvement to a previous work of the authors where a transition, model, an activity recognition method, a recovery method for the particle filter, and an improved density estimation.
@@ -13,8 +13,7 @@ The novelty of the paper was collected in the reading and it should be more clea
The rapid computation declaration is not proven, given that the authors do not compare the non-gridded approach timings.
-> The terminology "rapid computation scheme" only refers to the state estimation process, not the underlying mesh or the complete system performance. It seems this was not clearly formulated within the paper. Thanks for pointing that out. We tried to clarify this in different parts of the work, especially in the introduction. We also removed the terminology "rapid computation" and instead called it "approximation" scheme.
For clarification, the weighted-average estimator yields faster estimates of the position compared to the KDE approach as we have shown in our previous work "Fast Kernel Density Estimation using Gaussian
Filter Approximation". This previous work does also provide an extensive comparison between other state-of-the-art KDE approximations.
For clarification, the weighted-average estimator yields faster estimates of the position compared to the KDE approach as we have shown in our previous work "Fast Kernel Density Estimation using Gaussian Filter Approximation". This previous work does also provide an extensive comparison between other state-of-the-art KDE approximations.
Does the system will also work in regular buildings? A final comment on the lessons learned in this case of the 13th century building should be in the conclusions, given that the title focus on this very specific context.
@@ -89,8 +88,7 @@ Ln 237: "...the average acceleration..." This includes both linear acceleration
Ln 258 - This equation needs revision. Should it be "p(s_i|p) ~ N(u_i,p , std²_wifi)" ? Also the wall-attenuation-factor-model only takes into account attenuation by floors, not walls.
-> The equation is correct. Its the actual >result< of the normal distribution when questioned for the received s_i, given the model prediction was u_i,p with uncertainty \sigma^2_wifi.
-> We now made clear that our model is something in between the log-distance and the wall-attenuation factor model. To reduce computation time on the smartphone, only floors/ceilings are considered
as this can be achieved without costly intersection tests. We also pointed out, that including walls would be more accurate, but is costly during runtime (intersection-tests).
-> We now made clear that our model is something in between the log-distance and the wall-attenuation factor model. To reduce computation time on the smartphone, only floors/ceilings are considered as this can be achieved without costly intersection tests. We also pointed out, that including walls would be more accurate, but is costly during runtime (intersection-tests).
Ln 271-272: The authors mention that their WiFi fingerprinting approximation process is faster than classical fingerprinting, but they fail to provide a reference for an example of the latter or significant metrics such as the average time per square meter for fingerprinting a whole building. Furthermore, the authors should also take into account that while there are approaches where reference measurements are recorded on small grids between 1 to 2m, there are also approaches able to record reference measurements using faster methods. One example is walking by the building while registering ground truth points and using dead reckoning techniques (see Guimarães, V. et al. A motion tracking solution for indoor localization using smartphones. In Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)).