Abstract

In this paper, we propose an energy-efficient approach for machine-learning based continuous testing and monitoring of long-term patients at the wireless edge. The approach is applicable for any wearable sensors that generate time-series data. Our scheme simultaneously performs sensor-server clustering while ensuring the delay requirements of every user are met. In contrast to previous works on task offloading for generic edge computing/machine learning, our proposed model considers application specific parameters including the sampling rate, measurement duration and number of input channels/leads. We formulate the problem as a mixed integer nonlinear program (MINLP) and propose a heuristic solution. Two applications, cardiac event prediction from a wearable electrocardiograms (ECG), and epileptic seizure prediction from wearable scalp electroencephalography (EEG) are used to demonstrate the …