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I have measurements from two tablet sensors. The first sensor measures the touch pressure applied to the tablet and the second sensor is the acceleration of the tablet (in z direction). The sensor data are stored in csv files where the first column contains the timestamp (in milliseconds) and the second column contains the sensor data (either touch pressure or acceleration). I have uploaded the two sensors files: https://1drv.ms/f/s!AlkQuYgB1McYgudFUrpMzMopUr2wVw

Now I would like to investigate if there exists a correlation between touch pressure and acceleration. I could just calculate the cross-correlation or plotting the pressure against the acceleration (and perhaps add a linear regression line).

The problem is that the acceleration measurements have a more or less constant sampling rate but for the pressure I have not a constant sampling rate. That means that the number of measurements for acceleration and pressure is also not the same (and the timestamps are therefore also not the same).

How can I still calculate the correlation and producing a accleration vs. pressure plot? I thought about a interpolation of the accleration measurements to the timestamps of the pressure readings but I don't know what type of interpolation to use (linear or cubic seems not to be right). Perhaps there is a better way of doing it.

Here are the plots of the two sensors:

enter image description here

enter image description here

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If you are using MATLAB, there is a built in object type, called

timeseries

, that is very useful for this sort of problem.

>    Methods for class timeseries:
> 
> addevent              getabstime            gettsbeforeatevent    mean
> pvset                 times                  addsample            
> getdatasamples        gettsbeforeevent      median               
> rdivide               timeseries             append               
> getdatasamplesize     gettsbetweenevents    min                  
> resample              tsprops                createTstoolNode     
> getinterpmethod       hasduplicatetimes     minus                 set 
> utArithCommonData      delevent              getprop              
> idealfilter           mldivide              setabstime           
> utArithCommonOutput    delsample             getqualitydesc       
> init                  mode                  setinterpmethod      
> utArithCommonTime      detrend               getsamples            iqr
> mrdivide              setprop               utChkforSlashInName   
> display               getsampleusingtime    isequal              
> mtimes                setuniformtime        utGetEventTime         eq 
> gettsafteratevent     isequalwithequalnans  plot                  std 
> utStatCalculation      filter                gettsafterevent      
> ldivide               plus                  sum                   var 
> get                   gettsatevent          max                  
> pvget                 synchronize           
> 
> Static methods:
> 
> loadobj               tsChkTime             tsgetrelativetime

The setuniformtime method will do the interpolation for you. The other methods are often useful.

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