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.

中文翻译：

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一种用于医疗物联网心电图监测的自适应认知传感器节点

医疗物联网 (IoMT) 范式正在成为多项临床试验和医疗保健程序的主流。它依赖于新颖的非常精确和紧凑的传感设备和通信基础设施，开启了前所未有的数据收集和连续患者监测的可能性。然而，要充分发挥这项技术的潜力，还需要向前迈进一些。首先，必须将边缘计算范式添加到图片中。必须启用一定级别的近传感器处理，以提高 IoMT 节点的可扩展性、可移植性、可靠性和响应能力。其次，必须利用新颖的、越来越准确的数据分析算法，例如基于人工智能和深度学习的算法。为了实现这些目标，IoMT 节点的设计者、程序员、必须面对具有挑战性的优化任务，以便在功耗和电池寿命预算紧张的低功耗可穿戴和便携式处理系统上执行相当复杂的计算任务。在这项工作中，我们探索了认知数据分析算法在资源受限计算平台上的实现。为了最大限度地降低功耗，我们添加了一个自适应层，动态管理设备的硬件和软件配置，使其在运行时适应所需的操作模式。我们已经在使用卷积神经网络对低功耗微控制器上的心电图 (ECG) 轨迹进行分类的用例上评估了我们的方法。