Abstract-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 singlecore architecture, when performing advanced bio-signal analysis.

Abstract-Personalized healthcare devices enable low-cost, unobtrusive and long-term acquisition of clinically-relevant biosignals. These appliances, termed Wireless Body Sensor Nodes (WBSNs), are fostering a revolution in health monitoring for patients affected by chronic ailments. Nowadays, WBSNs often embed complex digital processing routines, which must be performed within an extremely tight energy budget. Addressing this challenge, in this paper we introduce a novel computing architecture devoted to the ultra-low power analysis of biosignals. Its heterogeneous structure comprises multiple processors interfaced with a shared acceleration resource, implemented as a Coarse-Grained Reconfigurable Array (CGRA). The CGRA mesh effectively supports the execution of the intensive loops that characterize bio-signal analysis applications, while requiring a low reconfiguration overhead. Moreover, both the processors and the reconfigurable fabric feature Single-Instruction / MultipleData (SIMD) execution modes, which increase efficiency when multiple data streams are concurrently processed. The run-time behavior on the system is orchestrated by a light-weight hardware mechanism, which concurrently synchronizes processors for SIMD execution and regulates access to the reconfigurable accelerator. By jointly leveraging run-time reconfiguration and SIMD execution, the illustrated heterogeneous system achieves, when executing complex bio-signal analysis applications, speedups of up to 11.3x on the considered kernels and up to 37.2% overall energy savings, with respect to an ultra-low power multicore platform which does not feature CGRA acceleration.

Abstract-Compressed Sensing (CS) is a new acquisitioncompression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power readout systems for wireless bio-signal monitoring devices, where large amounts of sensor data need to be transferred through power-hungry wireless links.Lately CS has been successfully applied for real-time energyaware single-lead ECG compression on resource-constrained Wireless Body Sensor Network (WBSN) motes [1]. Building on our previous work, in this paper we propose a new and promising approach for joint compression of multi-lead ECG signals, where strong correlations exist between them. This situation that exhibit strong correlations, can be exploited to reduce even further amount of data to be transmitted wirelessly, thus addressing the important challenge of ultra-low-power embedded monitoring of multi-lead ECG signals.I. INTRODUCTION CS is a new sensing and processing paradigm, which challenges the traditional analog-to-digital conversion based on the Shannon sampling theorem. For sparse signals such as the electrocardiogram (ECG), Nyquist-rate sampling produces a large amount of redundant digital samples, which are costly to wirelessly transmit in the context of our target mobile ECG monitoring systems, and require to be further compressed using non-linear digital techniques. CS is a methodology that has been recently proposed to address this problem.Capitalizing on this sparsity, we have recently proposed [1], [2], to apply the emerging compressed sensing (CS) approach [3] for a low-complexity, real-time and energy-efficient ECG signal compression on WBSN motes. We have also quantified the potential of compressed sensing (CS) for lowcomplexity energy-efficient ECG compression on the stateof-the-art Shimmer TM WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-ofthe-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. The results validate the suitability of compressed sensing for real-time energy-aware ECG compression on resource-constrained WBSN motes.Nonetheless, in a real scenario many of the bio-signals are acquired on multiple channels. In these cases, sampling, compressing and reconstructing each signal individually is clearly sub-optimal, because leads are not independent sources, and are in fact strongly correlated. In the case of the ECG acquisitions, all signals can be considered as different projections of a single multidimensional source, which is the electrical

Abstract-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 subject's 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.

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