Runtime adaptive iomt node on multi-core processor platform

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved

An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edge-computing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset

Pollinator convergence and the nature of species' boundaries in sympatric Sardinian Ophrys (Orchidaceae)

Background and Aims In the sexually deceptive Ophrys genus, species isolation is generally considered ethological and occurs via different, specific pollinators, but there are cases in which Ophrys species can share a common pollinator and differ in pollen placement on the body of the insect. In that condition, species are expected to be reproductively isolated through a pre-mating mechanical barrier. Here, the relative contribution of pre- vs. post-mating barriers to gene flow among two Ophrys species that share a common pollinator and can occur in sympatry is studied. Methods A natural hybrid zone on Sardinia between O. iricolor and O. incubacea, sharing Andrena morio as pollinator, was investigated by analysing floral traits involved in pollinator attraction as odour extracts both for non-active and active compounds and for labellum morphology. The genetic architecture of the hybrid zone was also estimated with amplified fragment length polymorphism (AFLP) markers, and pollination fitness and seed set of both parental species and their hybrids in the sympatric zone were estimated by controlled crosses. Key Results Although hybrids were intermediate between parental species in labellum morphology and non-active odour compounds, both parental species and hybrids produced a similar odour bouquet for active compounds. However, hybrids produced significantly lower fruit and seed set than parental species, and the genetic architecture of the hybrid zone suggests that they were mostly first-generation hybrids. Conclusions The two parental species hybridize in sympatry as a consequence of pollinator overlap and weak mechanical isolation, but post-zygotic barriers reduce hybrid frequency and fitness, and prevent extensive introgression. These results highlight a significant contribution of late post-mating barriers, such as chromosomal divergence, for maintaining reproductive isolation, in an orchid group for which pre-mating barriers are often considered predominan

An Adaptable Cognitive Microcontroller Node for Fitness Activity Recognition

The new generation of wireless technologies, fitness trackers, and devices with embedded sensors can have a big impact on healthcare systems and quality of life. Among the most crucial aspects to consider in these devices are the accuracy of the data produced and power consumption. Many of the events that can be monitored, while apparently simple, may not be easily detectable and recognizable by devices equipped with embedded sensors, especially on devices with low computing capabilities. It is well known that deep learning reduces the study of features that contribute to the recognition of the different target classes. In this work, we present a portable and battery-powered microcontroller-based device applicable to a wobble board. Wobble boards are low-cost equipment that can be used for sensorimotor training to avoid ankle injuries or as part of the rehabilitation process after an injury. The exercise recognition process was implemented through the use of cognitive techniques based on deep learning. To reduce power consumption, we add an adaptivity layer that dynamically manages the device’s hardware and software configuration to adapt it to the required operating mode at runtime. Our experimental results show that adjusting the node configuration to the workload at runtime can save up to 60% of the power consumed. On a custom dataset, our optimized and quantized neural network achieves an accuracy value greater than 97% for detecting some specific physical exercises on a wobble board

Runtime-adaptive cognitive IoT nodes

Current mainstream approach to sensor data monitoring usually relies on cloud access: samples are acquired by connected devices and data processing is performed on remote servers. To improve responsiveness, security and resilience, devices and programming methodologies must be improved, with the aim of enabling data analytics at the edge. Unfortunately this is not an easy task, especially in the IoT domain. In this paper, we present a research approach that manages at runtime the hardware/software configuration of a low-power processing system, with the aim of adapting to dynamically changing workloads optimizing power-relevant settings to the corresponding operating point. First, we present a first validation experiment, involving 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 lightweight 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. Second we present our plans to extend this approach to more complex multi-core systems