Hardware/Software Co-Design of Ultra-Low Power Biomedical Monitors


Ongoing changes in world demographics and the prevalence of unhealthy lifestyles are imposing a paradigm shift in healthcare delivery. Nowadays, chronic ailments such as cardiovascular diseases, hypertension and diabetes, represent the most common causes of death according to the World Health Organization. It is estimated that 63% of deaths worldwide are directly or indirectly related to these non-communicable diseases (NCDs), and by 2030 it is predicted that the health delivery cost will reach an amount comparable to 75% of the current GDP. In this context, technologies based on Wireless Sensor Nodes (WSNs) effectively alleviate this burden enabling the conception of wearable biomedical monitors composed of one or several devices connected through a Wireless Body Sensor Network (WBSN). These resourceconstrained systems allow for long term recording of biological signals and perform embedded advanced digital signal processing (DSP) enabling autonomous diagnosis even outside a hospital environment. Energy efficiency is of paramount importance for these devices, which must operate for prolonged periods of time with a single battery charge. Therefore, in order to minimize power consumption, both the software executing in these platforms and the underlying hardware require a carefully tailored design. In this thesis I propose a set of hardware/software co-design techniques to drastically increase the energy efficiency of biomedical monitors. To this end, I jointly explore different alternatives to reduce the required computational effort at the software level while optimizing the power consumption of the processing hardware by employing ultra-low power multi-core architectures that exploit DSP application characteristics. First, at the sensor level, I study the utilization of a heartbeat classifier to perform selective advancedDSP on state-of-the-art ECG biomedical monitors. To this end, I developed a framework to design and train real-time, lightweight heartbeat neuro-fuzzy classifiers, detailing the required optimizations to efficiently execute them on a resource-constrained platform. Then, at the network level I propose a more complex transmission-aware WBSN for activity monitoring that provides different tradeoffs between classification accuracy and transmission volume. In this work, I study the combination of a minimal set of WSNs with a smartphone, and propose two classification schemes that trade accuracy for transmission volume. The proposed method can achieve accuracies ranging from 88% to 97% and can save up to 86% of wireless transmissions, outperforming the state-of-the-art alternatives. Second, I propose a synchronization-based low-powermulti-core architecture for bio-signal processing. I introduce a hardware/software synchronization mechanism that allows to achieve high energy efficiency while parallelizing the execution of multi-channel DSP appli-

Cite this paper

@inproceedings{Lopez2016HardwareSoftwareCO, title={Hardware/Software Co-Design of Ultra-Low Power Biomedical Monitors}, author={Rub{\'e}n Braojos Lopez}, year={2016} }