Rubén Braojos

Ultra-Low Power Design of Wearable Cardiac Monitoring Systems

This paper presents the system-level architecture of novel ultra-low power wireless body sensor nodes (WBSNs) for real-time cardiac monitoring and analysis, and discusses the main design challenges of this new generation of medical devices. In particular, it highlights first the unsustainable energy cost incurred by the straightforward wireless streaming of raw data to external analysis servers. Then, it introduces the need for new cross-layered design methods (beyond hardware and software boundaries) to enhance the autonomy of WBSNs for ambulatory monitoring. In fact, by embedding more onboard intelligence and exploiting electrocardiogram (ECG) specific knowledge, it is possible to perform real-time compressive sensing, filtering, delineation and classification of heartbeats, while dramatically extending the battery lifetime of cardiac monitoring systems. The paper concludes by showing the results of this new approach to design ultra-low power wearable WBSNs in a real-life platform commercialized by SmartCardia. This wearable system allows a wide range of applications, including multi-lead ECG arrhythmia detection and autonomous sleep monitoring for critical scenarios, such as monitoring of the sleep state of airline pilots.

Hardware/software approach for code synchronization in low-power multi-core sensor nodes

Latest embedded bio-signal analysis applications, targeting low-power Wireless Body Sensor Nodes (WBSNs), present conflicting requirements. On one hand, bio-signal analysis applications are continuously increasing their demand for high computing capabilities. On the other hand, long-term signal processing in WBSNs must be provided within their highly constrained energy budget. In this context, parallel processing effectively increases the power efficiency of WBSNs, but only if the execution can be properly synchronized among computing elements. To address this challenge, in this work we propose a hardware/software approach to synchronize the execution of bio-signal processing applications in multi-core WBSNs. This new approach requires little hardware resources and very few adaptations in the source code. Moreover, it provides the necessary flexibility to execute applications with an arbitrarily large degree of complexity and parallelism, enabling considerable reductions in power consumption for all multi-core WBSN execution conditions. Experimental results show that a multi-core WBSN architecture using the illustrated approach can obtain energy savings of up to 40%, with respect to an equivalent single-core architecture, when performing advanced bio-signal analysis.

A methodology for embedded classification of heartbeats using random projections

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subjects bio-signals. One of its most relevant applications is the acquisition and analysis of Electrocardiograms (ECGs). These low-power WBSN designs, while able to perform advanced signal processing to extract information on hearth conditions of subjects, are usually constrained in terms of computational power and transmission bandwidth. It is therefore beneficial to identify in the early stages of analysis which parts of an ECG acquisition are critical and activate only in these cases detailed (and computationally intensive) diagnosis algorithms. In this paper, we introduce and study the performance of a real-time optimized neuro-fuzzy classifier based on random projections, which is able to discern normal and pathological heartbeats on an embedded WBSN. Moreover, it exposes high confidence and low computational and memory requirements. Indeed, by focusing on abnormal heartbeats morphologies, we proved that a WBSN system can effectively enhance its efficiency, obtaining energy savings of as much as 63% in the signal processing stage and 68% in the subsequent wireless transmission when the proposed classifier is employed.

A Wireless Body Sensor Network for Activity Monitoring with Low Transmission Overhead

Activity recognition has been a research field of high interest over the last years, and it finds application in the medical domain, as well as personal healthcare monitoring during daily home- and sports-activities. With the aim of producing minimum discomfort while performing supervision of subjects, miniaturized networks of low-power wireless nodes are typically deployed on the body to gather and transmit physiological data, thus forming a Wireless Body Sensor Network (WBSN). In this work, we propose a WBSN for online activity monitoring, which combines the sensing capabilities of wearable nodes and the high computational resources of modern smart phones. The proposed solution provides different tradeoffs between classification accuracy and energy consumption, thanks to different workloads assigned to the nodes and to the mobile phone in different network configurations. In particular, our WBSN is able to achieve very high activity recognition accuracies (up to 97.2%) on multiple subjects, while significantly reducing the sampling frequency and the volume of transmitted data with respect to other state-of-the-art solutions.

Embedded real-time ECG delineation methods: A comparative evaluation

Wireless sensor nodes (WSNs) have recently evolved to include a fair amount of computational power, so that advanced signal processing algorithms can now be embedded even in these extremely low-power platforms. An increasingly successful field of application of WSNs is tele-healthcare, which enables continuous monitoring of subjects, even outside a medical environment. In particular, the design of solutions for automated and remote electrocardiogram (ECG) analysis has attracted considerable research interest in recent years, and different algorithms for delineation of normal and pathological heart rhythms have been proposed. In this paper, some of the most promising techniques for filtering and delineation of ECG signals are explored and comparatively evaluated, describing their implementation on the state-of-the-art IcyHeart WSN. The goal of this paper is to explore the trade-offs implied in the different settings and the impact of design choices for implementing “smart” WSNs dedicated to monitoring ECG bio-signals.

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subjects bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

Synchronizing code execution on ultra-low-power embedded multi-channel signal analysis platforms

Embedded biosignal analysis involves a considerable amount of parallel computations, which can be exploited by employing low-voltage and ultra-low-power (ULP) parallel computing architectures. By allowing data and instruction broadcasting, single instruction multiple data (SIMD) processing paradigm enables considerable power savings and application speedup, in turn allowing for a lower voltage supply for a given workload. The state-of-the-art multi-core architectures for biosignal analysis however lack a bare, yet smart, synchronization technique among the cores, allowing lockstep execution of algorithm parts that can be performed using the SIMD, even in the presence of data-dependent execution flows. In this paper, we propose a lightweight synchronization technique to enhance an ULP multi-core processor, resulting in improved energy efficiency through lockstep SIMD execution. Our results show that the proposed improvements accomplish tangible power savings, up to 64% for an 8-core system operating at a workload of 89 MOps/s while exploiting voltage scaling.

A Synchronization-Based Hybrid-Memory Multi-Core Architecture for Energy-Efficient Biomedical Signal Processing

In the last decade, improvements on technology scaling have enabled the design of a novel generation of wearable bio-sensing monitors. These smart Wireless Body Sensor Nodes (WBSNs) are able to acquire and process biological signals, such as electrocardiograms, for periods of time extending from hours to days. The energy required for the on-node digital signal processing (DSP) is a crucial limiting factor in the conception of these devices. To address this design challenge, we introduce a domain-specific ultra-low power (ULP) architecture dedicated to bio-signal processing. The platform features a light-weight strategy to support different operating modes and synchronization among cores. Our approach effectively reduces the power consumption, harnessing the intrinsic parallelism and the workload requirements characterizing the target domain. Operations at low voltage levels are supported by a heterogeneous memory subsystem comprising a standard-cell based ultra-low voltage reliable partition. Experimental results show that, when executing real-world bio-signal DSP applications, a state-of-the-art multi-core architecture can improve its energy efficiency in up to 50 percent by utilizing our proposed approach, outperforming traditional single-core alternatives.

Nano-engineered architectures for ultra-low power wireless body sensor nodes

Wireless body sensor nodes (WBSNs) are miniaturized devices that are able to acquire, process and transmit bio-signals (such as electrocardiograms, respiration or human-body kinetics). WBSNs face major design challenges due to extremely limited power budgets and very small form factors. We demonstrate, for the first time in the literature, the use of disruptive nanotechnologies to create new nano-engineered ultra-low power (ULP) WBSN architectures. Compared to state-of-the-art multi-core WBSN designs, our new architectures dramatically reduce power consumption by 5.42× and footprint by 5×, while fulfilling real-time processing requirements of bio-signal monitoring applications. Our WBSN architectures achieve these results by utilizing emerging non-volatile memory technologies (such as resistive RAM and spin-transfer torque RAM) and their ultra-dense and fine-grained three-dimensional integration with logic (such as monolithic three-dimensional integration naturally enabled by carbon nanotube field-effect transistors).

A multi-core reconfigurable architecture for ultra-low power bio-signal analysis

This paper introduces a novel computing architecture devoted to the ultra-low power analysis of multiple bio-signals. Its structure comprises several processors interfaced with a shared acceleration resource, implemented as a Coarse Grained Reconfigurable Array (CGRA). The CGRA supports the efficient execution of the computationally intensive kernels present in this application domain, while requiring a low reconfiguration overhead. The run-time behavior of the resulting heterogeneous system is orchestrated by a light-weight hardware mechanism, which concurrently synchronizes processors and regulates access to the reconfigurable accelerator. The architecture achieves speed-ups of up to 11× on different bio-signal processing kernels and system-level energy savings of up to 18.6%, with respect to a multi-core platform, which does not feature CGRA acceleration.