If clipping occurred for any finite soan of time and the vibration peak is unbounded, then the actual mean value could be anything (large). However the peak might be bounded, under the assumption that you were not killed by the peak acceleration, nor the bike destroyed. That assumption would limit the possible range of the mean.

If data is missing, then one possibility is to model the data or data distribution, then do a best fit of the model parameters to the valid portion of the data, and take the mean of the model. For instance you could try fitting the the data to suspected statistical distributions (a clipped Gaussian, etc.) with a known mean. However, whether this works depends on whether the model describes the actual accelerations before clipping.

If clipping occurred for any finite span of time and the vibration peak is unbounded, then the actual mean value could be anything (large). However the peak might actually be bounded, under the assumption that you were not killed by the peak acceleration, nor the bike destroyed. That assumption about the maxima of the peaks would limit the possible range of the mean.

If data is missing, then one possibility is to model the data or data distribution, then do a best fit of the model parameters to the valid portion of the data, and take the mean of the model. For instance you could try fitting the the data to suspected statistical distributions (a clipped Gaussian, etc.) with a known mean. However, whether this works depends on whether the model describes the actual accelerations before clipping.