A Methodology for the Systematic Evaluation of ANN Classifiers for BSN Applications

While many BSN applications require that sensor nodes be able to operate for extended periods of time, they also often require the wireless transmission of copious amounts of sensor data to a data aggregator or base station, where the raw data is processed into application-relevant information. The energy requirements of such streaming can be prohibitive, given the competing considerations of form factor and battery life requirements. Making intelligent decisions on the node about which data to store or transmit, and which to ignore, is a promising method of reducing energy consumption. Artificial neural network (ANN) classifiers are among several competitive techniques for such data selection. However, no systematic metrics exist for determining if an ANN classifier is suited for a particular resource constrained computing environment of a typical BSN node. An especially difficult task is assessing, at the design stage, which classifier architectures are feasible on a given resource-constrained node, what computational resources are required to execute a given classifier, and what classification performance might be achieved by a particular classifier on a given set of resources. This paper describes techniques for quantifying and predicting the performance of ANN classifiers on wearable sensor nodes using scalable synthetic test data. Additionally, the paper shows a comparison of synthetic data with gait data collected using an inertial BSN node, and classification results of the gait data using a cerebellar model arithmetic computer (CMAC) architecture show excellent agreement with theoretical predictions.