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Autonomous microsystems often operate under both strict power and energy constraints and substantial environmental variation. To improve sensing and control performance, parameter identification techniques can be useful if they may be implemented within an appropriate power budget. While it is well known that identification algorithm performance depends on sampling rate, for energy minimization sensor power models and rates of parameter adaptation can also significantly influence optimal sensor usage. In this paper, an empirical, simulation-generated model is found for parameter error of recursive least square identification of a prototypical second-order linear continuous system as a function of sampling rate, number of samples, and sensor noise density. This model is coupled with representative power models of certain common sensing circuits used in microelectromechanical systems (MEMS) to recommend optimal sensing schemes for low-power parameter identification. A case study of a walking micro-robot is presented.