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Wearable and implantable medical devices ranging from wellness monitors to deep brain stimulators are becoming increasingly vital and ubiquitous. Such devices continuously take measurements, which consumes battery. The power consumption is proportional to the amount of information collected and with the frequency of data collection. High power consumption leads to rapid discharging of battery limiting the usage of these devices. These signals are often transmitted wirelessly for analysis, as well as to keep track of the user’s record, which also significantly increases power consumption. In this project, we evaluated adaptively modifying the rate of data collection on these devices, in other words, the sampling rate, for electrophysiological monitoring as the relevance of the signal changes in time. We carried out these tests using a proof-of-concept prototype developed for this project. In particular, we reviewed the effects of such adaptive sampling on intracellular potentials, and motor unit action potentials (MUAPs). By doing so, we were able to reduce the amount of data by 48.95% and power by 41.50% for the MUAPs with an 8% sample loss within MUAPs, and by 69.20% and 57.14% for intracellular potentials with a 6.75% sample loss.