Pareto-Optimal Signal Processing On Low-Power Microprocessors

Miniaturised wireless body sensors equipped with low-power microcontrollers are used in various energy-constrained applications. The signal-processing algorithms often require running in real-time on a low computational and memory budget. In this paper we present a framework for the exploration of the design space of resource-efficient signal processing suitable for embedded processors. Using a velocity estimation algorithm for an athlete, we show which configurations of the algorithm perform best in respect to classification accuracy and runtime. Altering the sampling frequency, the feature combination, the classifier (Artificial Neural Network (ANN), Decision Tree (DT)), or the classifier's parametrisation, we obtained 15 Pareto-optimal configurations out of 1008 simulations. The highest classification accuracy of 93.92% was obtained using an ANN, and required 22422 clock cycles per classification. The lowest cycle count of 204 was obtained with a DT configuration which resulted in 84.66% accuracy.