A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients
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We developed a ballistocardiography (BCG)-based Internet-of-Medical-Things (IoMT) system for remote monitoring of cardiopulmonary health. The system composes of BCG sensor, edge node, and cloud platform. To improve computational efficiency and system stability, the system adopted collaborative computing between edge nodes and cloud platforms. Edge nodes undertake signal processing tasks, namely approximate entropy for signal quality assessment, a lifting wavelet scheme for separating the BCG and respiration signal, and the lightweight BCG and respiration signal peaks detection. Heart rate variability (HRV), respiratory rate variability (RRV) analysis and other intelligent computing are performed on cloud platform. In experiments with 25 participants, the proposed method achieved a mean absolute error (MAE)±standard deviation of absolute error (SDAE) of 9.6±8.2 ms for heartbeat intervals detection, and a MAE±SDAE of 22.4±31.1 ms for respiration intervals detection. To study the recovery of cardiopulmonary function in patients with coronavirus disease 2019 (COVID-19), this study recruited 186 discharged patients with COVID-19 and 186 control volunteers. The results indicate that the recovery performance of the respiratory rhythm is better than the heart rhythm among discharged patients with COVID-19. This reminds the patients to be aware of the risk of cardiovascular disease after recovering from COVID-19. Therefore, our remote monitoring system has the ability to play a major role in the follow up and management of discharged patients with COVID-19.Keywords:
Ballistocardiography
This paper is a method for efficient data processing trend prediction in edge computing, where many studies have recently been conducted. For distributed processing in edge computing, offloading method from each edge must be processed within the limited computing power of the edge. Thus, in the user devices, it needs to efficiently select the edge in consideration of edge performance and data processing trend with MDP. This paper provides an efficient offloading scheme by selecting edges and distributing the traffic in the edge. As a result, our method is to offload effectively to the edge because it considers both data processing trends based on MDP and the performance of the edge compared with the existing offloading methods.
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Effective control of emerging cyberphysical systems such as smart transportation, smart health-care, etc. requires edge computing infrastructure that is often organized into three layers, namely edge (IoT) devices, edge controllers (ECs) and the cloud. In large infrastructures, ECs must be deployed densely in the proximity of edge devices and need to satisfy strict constraints on cost, size, cooling, etc. Thus, ECs cannot host large amounts of local storage and instead must make use of cloud storage in the background to provide an impression of large, fast local storage to host the IoT device data needed for online and real-time queries. In this paper, we provide insights into the configuration issues of such an edge storage infrastructure (ESI) based on the evaluation of commercial ESIs on several real-world edge computing workloads. We also show that the current ESI designs are lacking in several respects, and suggest some approaches for enhancing their capabilities to meet the stringent requirements of emerging edge computing applications.
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本解説では，ブレインモルフィックコンピューティングハードウェア（Brainmorphic Computing HW）およびHuman-centric Edge AIの2つの話題と，それらの相互関係について述べる．ブレインモルフィックコンピューティングは，脳の情報処理原理に構成論的に迫ることを目指した新しいコンピューティングパラダイムである．これを実現するためには，現状のノイマン型デジタルコンピューティングHWとは異なり，脳科学的なアプローチにより，脳に特異的な機能群に対応する脳型アーキテクチャ群を，脳型HWに適した新規機能デバイスの物理・ダイナミクスを活用して効率的に実装する，ボトムアップ的アプローチを取る必要がある．一方，近未来のHuman-centric Edge AIパラダイムとは，現在のEdge AIあるいはEdgeコンピューティングの先にあるAIパラダイムであり，それぞれの個人の，個人的な問題に対し，個人的なデバイスにより，必要な時（だけ）に，できる限りローカルに対処するEdge AIパラダイムである．このためには，非常に小型で，非常に低消費電力なEdge HWが必要となる．ただし，HWに汎用性は求められず，必要な機能をいかに効率的に実装するかがカギとなる．最後に，これら2つの間の密接な相互関係について紹介する．
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The evolution of the Internet of Things (IoT) has augmented the necessity for Cloud, edge and fog platforms. The chief benefit of cloud-based schemes is they allow data to be collected from numerous services and sites, which is reachable from any place of the world. The organizations will be benefited by merging the cloud platform with the on-site fog networks and edge devices and as result, this will increase the utilization of the IoT devices and end users too. The network traffic will reduce as data will be distributed and this will also improve the operational efficiency. The impact of monitoring in edge and fog computing can play an important role to efficiently utilize the resources available at these layers. This paper discusses various techniques involved for monitoring for edge and fog computing and its advantages. The paper ends with a case study to demonstarte the need of monitoring in fog and edge in the healthcare system.
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