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Advanced Machine Learning Tools and Methods for IoMT Sensor Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 18802

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Special Issue Editor

Dr. Simon James Fong

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Guest Editor

Faculty of Science and Technology, University of Macau, Macau 999078, China
Interests: E-commerce; data mining; business intelligence; intelligent agent technology; electronic governance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

By 2021, governments are targeting to pave a roadmap for leveraging the latest technologies to empower patients and healthcare workers, to link up sensor devices, to tap into the power of big data and artificial intelligence (AI), etc. This endeavor embraces the latest technologies, including Internet-of-Medical-Things (IoMT), new generations of cloud/fog-computing, AI, and machine learning, which have to be designed in order to satisfy the application requirements of real-time and critical streaming IoMT data.

The rise of IoMT capitalizes on the values of time and space reduction between detection, measurement, and treatment using connected sensors and powerful analytics. While the data feeds received by IoMT come continuously in massive volume and high speed, the capabilities of medical data analytics, machine learning, and AI must keep increasing at a pace faster than before in order to monitor and understand the patterns, context, and meaning of the measurements. Making sound and timely decisions in such healthcare applications is possible when IoMT combined with fast AI can rapidly generate actionable conclusions. This is essential for a wide spectrum of e-Health applications ranging from critical ICU applications to auto-bot telemedicine, medical condition detection, and therapeutic processes.

Sensors can track various critical metrics and alert caregivers to respond in time. Sensors combined with telemedicine make it even easier to help speed up recovery. Knowing what patients are doing in between visits can help to speed up the recovery time for post-surgical procedures. Sensors that track bodily parameters are getting increasingly sophisticated, with blood pressure, glucose levels, sweat, sleep quality, brainwave, and even emotion analysis. IoT infrastructure provides connectedness and logistics in delivering the measurements direct from the sensors instantly to the users and/or doctors. What lacks now is a new breed of AI that can make sense of the multi-modal continuous data streams.

In this Special Issue, research results are needed to advance the current IoMT technologies together with new and fast analytics for providing smarter, wider, quicker patient-oriented e-Health services in the near future.

The latest research breakthroughs, good-quality surveys, and practical use-cases in real-life scenarios are welcomed. Contributions to this Special Issue pertaining—but not limited—to the following are welcome:

IoMT sensors and architectures;
IoMT-based e-Health services and applications;
Cloud and edge computing for IoMT-based e-Health;
Innovative IoMT devices, instruments, and systems;
Data stream mining for IoMT-based e-Health;
Data analytics for IoMT-based e-Health;
Machine learning and AI for IoMT-based e-Health;
Ambient assisted living with IoMT;
Human activity recognition with IoMT;
IoMT for lifestyle, fitness monitoring, and rehabilitation;
IoMT for pandemic and epidemiological solutions;
IoMT decision support systems;
IoMT data fusion.

Prof. Dr. Simon James Fong
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Internet-of-Medical-Things
fast data mining and decision supports for IoMT
machine learning for IoMT
new IoMT sensors and devices
IoMT based e-Health applications

Published Papers (4 papers)

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Research

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16 pages, 18845 KiB

Open AccessArticle

Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

by Mohamed Esmail Karar, Bandar Alotaibi and Munif Alotaibi

Sensors 2022, 22(6), 2348; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062348 - 18 Mar 2022

Cited by 18 | Viewed by 3362

Abstract

Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work. Full article

(This article belongs to the Special Issue Advanced Machine Learning Tools and Methods for IoMT Sensor Applications)

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26 pages, 9722 KiB

Open AccessArticle

DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices

by Giovanni Schiboni, Juan Carlos Suarez, Rui Zhang and Oliver Amft

Sensors 2020, 20(21), 6104; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216104 - 27 Oct 2020

Cited by 4 | Viewed by 2144 | Correction

Abstract

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications. Full article

(This article belongs to the Special Issue Advanced Machine Learning Tools and Methods for IoMT Sensor Applications)

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24 pages, 1772 KiB

Open AccessArticle

Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems

by Affan Ahmed Toor, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan and Simon Fong

Sensors 2020, 20(7), 2131; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072131 - 09 Apr 2020

Cited by 29 | Viewed by 4872

Abstract

With the increasing popularity of the Internet-of-Medical-Things (IoMT) and smart devices, huge volumes of data streams have been generated. This study aims to address the concept drift, which is a major challenge in the processing of voluminous data streams. Concept drift refers to overtime change in data distribution. It may occur in the medical domain, for example the medical sensors measuring for general healthcare or rehabilitation, which may switch their roles for ICU emergency operations when required. Detecting concept drifts becomes trickier when the class distributions in data are skewed, which is often true for medical sensors e-health data. Reactive Drift Detection Method (RDDM) is an efficient method for detecting long concepts. However, RDDM has a high error rate, and it does not handle class imbalance. We propose an Enhanced Reactive Drift Detection Method (ERDDM), which systematically generates strategies to handle concept drift with class imbalance in data streams. We conducted experiments to compare ERDDM with three contemporary techniques in terms of prediction error, drift detection delay, latency, and ability to handle data imbalance. The experimentation was done in Massive Online Analysis (MOA) on 48 synthetic datasets customized to possess the capabilities of data streams. ERDDM can handle abrupt and gradual drifts and performs better than all benchmarks in almost all experiments. Full article

(This article belongs to the Special Issue Advanced Machine Learning Tools and Methods for IoMT Sensor Applications)

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Review

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32 pages, 3826 KiB

Open AccessReview

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

by Liping Xie, Zilong Li, Yihan Zhou, Yiliu He and Jiaxin Zhu

Sensors 2020, 20(21), 6318; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216318 - 05 Nov 2020

Cited by 45 | Viewed by 6710

Abstract

Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people. Full article

(This article belongs to the Special Issue Advanced Machine Learning Tools and Methods for IoMT Sensor Applications)

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