The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. It relies on novel very accurate and compact sensing devices and communication infrastructures, opening previously unmatched possibilities of implementing data collection and continuous patient monitoring. Nevertheless, to fully exploit the potential of this technology, some steps forwards 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, 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, 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 cognitive data analysis algorithm on resource-constrained computing platforms. 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. We have assessed our approach on a use-case using a convolutional neural network to classify electrocardiogram (ECG) traces on a low-power microcontroller. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption and a quantized neural network reaches an accuracy value higher than 98% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
翻译:医学事物(IOMT)的互联网模式正在多个临床试验和医疗保健程序中成为主流。 它依赖新型的非常精确和紧凑的感测装置和通信基础设施,开启了以前不匹配的数据收集和持续病人监测的可能性。 然而,为了充分利用这一技术的潜力,需要向前迈出一些步骤。 首先, 边分处理模式必须添加到图片中。 必须使接近感官处理达到某种程度, 以提高IMT节点的可缩放性、 便捷性、 可靠性和反应能力。 其次, 必须利用新颖的、 日益准确的数据分析算法, 如人工智能和深水学习的算法。 要达到这些目标, 设计者、 IOMT 节点的程序员必须面对挑战性的任务。 为了在低功率和便携式处理系统上执行相当复杂的计算任务, 电磁力和电磁力处理器处理器处理器的计算法, 我们探索在资源紧张的计算平台上采用认知数据分析算法的算法。 为了尽量减少电力消耗, 我们增加一个适应性层, 以动态管理50度电力网络的硬件和软件配置, 我们的电动变压计算系统将运行的电路调调整到电算, 。