A runtime-adaptive cognitive IoT node for healthcare monitoring

Wearable and energy efficient processing nodes, allowing for continuous remote monitoring of patient vital parameters, are mainstream in modern health-care practice. Most recent approaches to the development of such systems combine near-sensor data processing with cognitive computing, to improve at the same time communication efficiency, responsiveness and accuracy of the analysis of the sensed data. In this paper, we present a hardware-software architecture for a connected sensor-processing node that allows the set of in-place processing tasks to be executed to be remotely controllable by an external user. The designed system is capable of dynamically adapting its operating point to the selected computational load, to minimize power consumption. The benefits of the proposed approach are tested on a use-case involving ECG monitoring, that, when selected, performs ECG classification using a lightweigth convolutional neural network. Experimental results show how the proposed approach can provide more than 50% power consumption reduction for common ECG activity, with less than 2% memory footprint overhead and reconfiguring the system in less than 1 ms.