How to get RMS to Frequency chart for discrete samples of acceleration?
If your time domain data is in units of vertical acceleration, then you can simply multiply the normalized (divide by N) raw fft values times the complex conjugate of itself and then take the square root. Each result will be the rms acceleration at that frequency. 

Note, if your data is sampled at 100 Hz, you are going to see the content from 0 to 50 Hz of all frequencies that are within the analog bandwidth of your sampler. Any higher analog frequencies will "alias" into the 0 to 50 Hz band. I am not sure what you have for an anti-alias filter, but it certainly can't be a "brick-wall" filter right at 50 Hz. A properly designed filter may have hope of sufficiently rejecting signals above 60 Hz, in which case you can trust your data from 0 to 40 Hz (for example; I am more illustrating the need to understand this before interpreting your data).

Regarding "Normalized FFT"; some FFT implementations will have this built in while others you will need to normalize. You can test this by taking an FFT of 512 ones [1 1 1 1 ...1] and see if the first sample of the FFT is 512 or 1. If 512 then you need to divide the FFT result by the number of samples (512) to normalize the result.  And by "raw FFT" data I am referring to the 512 complex FFT samples, not the FFT frequency spectrum you plotted above which I assume is the power spectrum which already performed the complex conjugate multiplication and is only showing the positive frequency axis, likely with a dB vertical scale. 

Usually these limits versus frequency are also normalized to be per Hz bandwidth. The FFT noise bandwidth is 1 bin (without further windowing which is another topic of importance if you start seeing dominant tones). So if you sample at 100 Hz with 512 bins, then each FFT bin has a noise bandwidth of $100/512= 0.1953$ Hz.  A more accurate measurement would average the power (the complex conjugate multiplication of the normalized FFT) over 5.12 bins before taking the square-root to get the average rms value per Hz bandwidth. A simpler and more conservative approach (will bound the actual max limit) would be to multiply the power in each bin by 5.12 before taking the square-root. 

You may see spikes above the threshold in the time domain, but the limit is rms and over frequency, so those results will expect to be less.