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 singlecore architecture, when performing advanced bio-signal analysi

Conception et implémentation d'un réseau sans-fil pour la surveillance continue des signes vitaux

Les dépenses de santé augmentent continuellement année après année et prennent une grande partie du budget d’un pays. Pendant les soins médicaux, les signes vitaux, tels que le rythme cardiaque et la respiration, sont des paramètres clés qui sont surveillés en permanence. La toux est un indicateur important de plusieurs problèmes comme la maladie pulmonaire obstructive chronique (MPOC), et c’est aussi la principale raison pour laquelle les patients consultent un médecin. En fait, c’est un mécanisme de défense pulmonaire des voies respiratoires qui permet l’expulsion de substances indésirables et irritantes. Les capteurs de corps sans fil sont de plus en plus utilisés par les cliniciens et les chercheurs, dans un large éventail d’applications telles que le sport, l’ingénierie spatiale et la médecine. La surveillance des signes vitaux en temps réel peut considérablement augmenter la précision du diagnostic et peut permettre des méthodes de guérison automatiques, par exemple, la détection et l’arrêt des crises d’épilepsie ou de narcolepsie. Les paramètres respiratoires sont essentiels en oxygénothérapie, en milieu hospitalier et en surveillance ambulatoire, tandis que l’évaluation de la sévérité de la toux est essentielle pour traiter plusieurs maladies, comme la bronchopneumopathie chronique obstructive (BPCO). Dans cette thèse, un système de surveillance respiratoire sans fil de faible puissance avec détection de la toux est présenté. Ce système utilise des capteurs multimodaux, portables et sans-fils, conçus à l’aide de composants conventionnels disponibles dans le commerce. Ces capteurs portables utilisent une unité de mesure inertielle à 9 axes de faible puissance pour mesurer la fréquence respiratoire, et un microphone MEMS pour effectuer la détection de la toux. L’architecture de chaque capteur sans fil est présentée. De plus, les résultats montrent que le capteur à petite taille de 26,67 x 65,53 mm² consomme environ 12 à 16,2 mA et peut durer au moins 6 heures avec une batterie lithium-ion miniature de 100 mA. L’unité d’acquisition, l’unité de communication sans fil et les algorithmes de traitement de données sont décrits. Les performances du réseau de capteurs sont présentées pour des tests expérimentaux en comparant avec la pléthysmographie d’inductance respiratoire.Health care expenses are continuously increasing year after year and taking a large part of a country’s budget. During medical care, vital signs, such as heart and breathing rates, are key parameters that are continuously monitored. Coughing is a prominent indicator of several problems such as COPD, and it is also the main reason for why patients seek medical advice. In fact, it is a pulmonary defense mechanism of the respiratory tract that allows the expulsion of undesirable and irritating substances. Wireless body sensors are increasingly used by clinicians and researchers, in a wide range of applications such as sports, space engineering and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g. detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease (COPD). In this thesis, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off the shelf components. These wearable sensors use a low-power 9-axis inertial measurement unit to measure the respiratory frequency, and a MEMs microphone to perform cough detection. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small 26.67 x 65.53 mm² patch-sensor consumes around 12 to 16.2 mA, and can last at least 6 hours with a miniature 100 mA lithium ion battery. The acquisition unit, the wireless communication unit and the data processing algorithms are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmograph

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). Energy efficiency is of paramount importance for these devices, which must operate for prolonged periods of time with a single battery charge. In this thesis I propose a set of hardware/software co-design techniques to drastically increase the energy efficiency of bio-medical 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 advanced DSP on state-of-the-art ECG bio-medical monitors. To this end, I developed a framework to design and train real-time, lightweight heartbeat neuro-fuzzy classifiers, detail- ing 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-power multi-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 applications. Then, I generalize the methodology to support bio-signal processing applications with an arbitrarily high degree of parallelism. Due to the benefits of SIMD execution and software pipelining, the architecture can reduce its power consumption by up 38% when compared to an equivalent low-power single-core alternative. Finally, I focused on the optimization of the multi-core memory subsystem, which is the major contributor to the overall system power consumption. First I considered a hybrid memory subsystem featuring a small reliable partition that can operate at ultra-low voltage enabling low-power buffering of data and obtaining up to 50% energy savings. Second, I explore a two-level memory hierarchy based on non-volatile memories (NVM) that allows for aggressive fine-grained power gating enabled by emerging low-power NVM technologies and monolithic 3D integration. Experimental results show that, by adopting this memory hierarchy, power consumption can be reduced by 5.42x in the DSP stage