I have a dataset (~20k samples) of sensor data gathered from a smartphone. What I want to do with it is to detect those spikes you can see in the graphs below.
They occur when the user presses a button.

I want to label the data that refers to those spikes, but only this data. The other data is not relevant for my research task.
I read about using filters for this, but I honestly have no idea to do this.

[![enter image description here](https://i.stack.imgur.com/dpNoE.png)](https://i.stack.imgur.com/dpNoE.png)

How can I do this easily?
I work with `R` and `RapidMiner`.

During the training process, I created a predictive model for mobile sensor data to label sensor data. Now I want to apply the model on "real-world" data, but first I need to get filter out the samples I do not need.

Would it be possible to use a Support Vector Machines to do this? And if so, could someone explain how?

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> Here a sample set: [link](https://1drv.ms/u/s!Aos94Bv6Q4i8sNFdBkNq6nq1Z2rbxg)
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