Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Internet of Medical Things in Healthcare Applications

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 55488

Share This Special Issue

Special Issue Editors

Dr. Anthony Fleury

E-Mail Website
Guest Editor

Centre for Digital Systems, IMT Lille Douai, Université Lille, 59000 Lille, France
Interests: supervised classification methods; activity recognition; online classification; mHealth; pHealth; SmartHomes
Special Issues, Collections and Topics in MDPI journals

Dr. Carla Taramasco

E-Mail Website
Guest Editor

Escuela de Ingeniería Civil informática, Facultad de Ingenería, Universidad de Valparaíso. General Cruz 222, Valparaíso, Chile
Interests: ehealth; mheatlh; telemedicine; IoT; wearables and Big Data

Special Issue Information

Dear Colleagues,

Internet of Medical Things (IoMT) is an IoT submarket that groups all medical devices and applications capable of obtaining, analyzing, and sharing data over the internet. Among them are wearable devices, vital signs monitored at home or in health centers, and health telemonitoring,. IoMT has been widely adopted by health services thanks to the great positive impact generated in disease control and drug administration, improving treatment methods, reducing costs, and enhancing the quality of life and user experience in patients and medical staff.

The adoption of IoMT brings with it challenges such as interoperability between systems of different providers, in a way that the data can be analyzed to improve health diagnoses and applications, avoiding forming islands of data. Other challenges present in the implementation of IoMT, for example, are: having connectivity strategies so that the devices can interconnect with each other and with the cloud in a simple way; design devices (wearables or ambient based) for biomedical signals monitoring with reduced maintenance; improving the security and privacy of patient records; and network administration for IoMT devices and design of frameworks for monitoring systems in both health centers and homes.

The Special Issue “Internet of Medical Things in Healthcare Applications” aims to group innovative research, work in progress or surveys related to the topics described above that contribute to knowledge and improvements in IoMT. This Special Issue includes but is not limited to the following topics:

In-Hospital and in-home remote patient monitoring;
Innovative IoMT devices and wearable and/or ambient sensors;
IoMT cybersecurity;
Telehealth virtual consulting;
IoMT for administrative or clinical functions;
Collection, integration, and analysis of clinical data.

Dr. Anthony Fleury
Dr. Carla Taramasco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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
Remote patient monitoring
Telehealth
Smart healthcare
IoMT framework

Published Papers (13 papers)

Download All Papers

Research

Jump to: Review

15 pages, 3231 KiB

Open AccessArticle

Development of a Low-Power IoMT Portable Pillbox for Medication Adherence Improvement and Remote Treatment Adjustment

by Dimitrios Karagiannis, Konstantinos Mitsis and Konstantina S. Nikita

Sensors 2022, 22(15), 5818; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155818 - 04 Aug 2022

Cited by 10 | Viewed by 2620

Abstract

Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug–drug, food–drug, and supplement–drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

15 pages, 4260 KiB

Open AccessArticle

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

by Eko Sakti Pramukantoro and Akio Gofuku

Sensors 2022, 22(14), 5080; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145080 - 06 Jul 2022

Cited by 8 | Viewed by 2145

Abstract

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

14 pages, 3308 KiB

Open AccessArticle

Real-Time Internet of Medical Things System for Detecting Blood Leakage during Hemodialysis Using a Novel Multiple Concentric Ring Sensor

by Hsiang-Wei Hu, Chih-Hao Liu, Yi-Chun Du, Kuan-Yu Chen, Hsuan-Ming Lin and Chou-Ching Lin

Sensors 2022, 22(5), 1988; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051988 - 03 Mar 2022

Cited by 1 | Viewed by 2468

Abstract

Venous needle dislodgement (VND) is a major healthcare safety concern in patients undergoing hemodialysis. Although VND is uncommon, it can be life-threatening. The main objective of this study was to implement a real-time multi-bed monitoring system for VND by combining a novel leakage-detection device and IoMT (Internet of Medical Things) technology. The core of the system, the Acusense IoMT platform, consisted of a novel leakage-detection patch comprised of multiple concentric rings to detect blood leakage and quantify the leaked volume. The performance of the leakage-detection system was evaluated on a prosthetic arm and clinical study. Patients with a high risk of blood leakage were recruited as candidates. The system was set up in a hospital, and the subjects were monitored for 2 months. During the pre-clinical simulation experiment, the system could detect blood leakage volumes from 0.3 to 0.9 mL. During the test of the IoMT system, the overall success rate of tests was 100%, with no lost data packets. A total of 701 dialysis sessions were analyzed, and the accuracy and sensitivity were 99.7% and 90.9%, respectively. Evaluation questionnaires showed that the use of the system after training changed attitudes and reduced worry of the nursing staff. Our results show the feasibility of using a novel detector combined with an IoMT system to automatically monitor multi-bed blood leakage. The innovative concentric-circle design could more precisely control the warning blood-leakage threshold in any direction to achieve clinical cost-effectiveness. The system reduced the load on medical staff and improved patient safety. In the future, it could also be applied to home hemodialysis for telemedicine during the era of COVID-19. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

24 pages, 5897 KiB

Open AccessArticle

A Study Protocol for Occupational Rehabilitation in Multiple Sclerosis

by Marco Trombini, Federica Ferraro, Giulia Iaconi, Lucilla Vestito, Fabio Bandini, Laura Mori, Carlo Trompetto and Silvana Dellepiane

Sensors 2021, 21(24), 8436; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248436 - 17 Dec 2021

Viewed by 2680

Abstract

Digital medical solutions can be very helpful in restorative neurology, as they allow the patients to practice their rehabilitation activities remotely. This work discloses ReMoVES, an IoMT system providing telemedicine services, in the context of Multiple Sclerosis rehabilitation, within the frame of the project STORMS. A rehabilitative protocol of exercises can be provided as ReMoVES services and integrated into the Individual Rehabilitation Project as designed by a remote multidimensional medical team. In the present manuscript, the first phase of the study is described, including the definition of the needs to be addressed, the employed technology, the design and the development of the exergames, and the possible practical/professional and academic consequences. The STORMS project has been implemented with the aim to act as a starting point for the development of digital telerehabilitation solutions that support Multiple Sclerosis patients, improving their living conditions. This paper introduces a study protocol and it addresses pre-clinical research needs, where system issues can be studied and better understood how they might be addressed. It also includes tools to favor remote patient monitoring and to support the clinical staff. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

17 pages, 4473 KiB

Open AccessArticle

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

by Mahbub Ul Alam and Rahim Rahmani

Sensors 2021, 21(15), 5025; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155025 - 24 Jul 2021

Cited by 21 | Viewed by 4289

Abstract

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall,

F_{s c o r e}

for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

15 pages, 2685 KiB

Open AccessArticle

EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and Internet-Sourced Data

by Beakcheol Jang, Myeonghwi Kim, Inhwan Kim and Jong Wook Kim

Sensors 2021, 21(14), 4665; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144665 - 07 Jul 2021

Cited by 2 | Viewed by 2445

Abstract

Due to the prevalence of globalization and the surge in people’s traffic, diseases are spreading more rapidly than ever and the risks of sporadic contamination are becoming higher than before. Disease warnings continue to rely on censored data, but these warning systems have failed to cope with the speed of disease proliferation. Due to the risks associated with the problem, there have been many studies on disease outbreak surveillance systems, but existing systems have limitations in monitoring disease-related topics and internationalization. With the advent of online news, social media and search engines, social and web data contain rich unexplored data that can be leveraged to provide accurate, timely disease activities and risks. In this study, we develop an infectious disease surveillance system for extracting information related to emerging diseases from a variety of Internet-sourced data. We also propose an effective deep learning-based data filtering and ranking algorithm. This system provides nation-specific disease outbreak information, disease-related topic ranking, a number of reports per district and disease through various visualization techniques such as a map, graph, chart, correlation and coefficient, and word cloud. Our system provides an automated web-based service, and it is free for all users and live in operation. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

21 pages, 3757 KiB

Open AccessArticle

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

by Abdullah Lakhan, Mazin Abed Mohammed, Ahmed N. Rashid, Seifedine Kadry, Thammarat Panityakul, Karrar Hameed Abdulkareem and Orawit Thinnukool

Sensors 2021, 21(12), 4093; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124093 - 14 Jun 2021

Cited by 80 | Viewed by 4419

Abstract

The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

20 pages, 994 KiB

Open AccessArticle

Granular Data Access Control with a Patient-Centric Policy Update for Healthcare

by Fawad Khan, Saad Khan, Shahzaib Tahir, Jawad Ahmad, Hasan Tahir and Syed Aziz Shah

Sensors 2021, 21(10), 3556; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103556 - 20 May 2021

Cited by 10 | Viewed by 2518

Abstract

Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

15 pages, 1681 KiB

Open AccessArticle

DBOS: A Dialog-Based Object Query System for Hospital Nurses

by Edward T.-H. Chu and Zi-Zhe Huang

Sensors 2020, 20(22), 6639; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226639 - 19 Nov 2020

Cited by 5 | Viewed by 2860

Abstract

Due to the advance of indoor positioning technology, it is now possible to trace mobile medical equipment (such as electrocardiography machines, patient monitors, and so on) being moved around a hospital ward. With the support of an object tracking system, nurses can easily locate and find a device, especially when they prepare for a shift change or a medical treatment. As nurses usually face high workloads, it is highly desirable to provide nurses with a user-friendly search interface integrated into a popular mobile app that they use daily. For this, DBOS, a dialog-based object query system, is proposed, which simulates a real conversation with users via the Line messaging app’s chatbot interface. A hybrid method that combines cosine similarity (CS) and term frequency–inverse document frequency (TF-IDF) is used to determine user intent. The result is returned to the user through Line’s interface. To evaluate the applicability of DBOS, 70 search queries given by a head nurse were tested. DBOS was compared with CS, TF-IDF, and Facebook Wit.ai respectively. The experiment results show that DBOS outperforms the abovementioned methods and can achieve a 92.8% accuracy in identifying user intent. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

24 pages, 5450 KiB

Open AccessArticle

A Study on CP-ABE-Based Medical Data Sharing System with Key Abuse Prevention and Verifiable Outsourcing in the IoMT Environment

by Yong-Woon Hwang and Im-Yeong Lee

Sensors 2020, 20(17), 4934; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174934 - 31 Aug 2020

Cited by 15 | Viewed by 4157

Abstract

Recent developments in cloud computing allow data to be securely shared between users. This can be used to improve the quality of life of patients and medical staff in the Internet of Medical Things (IoMT) environment. However, in the IoMT cloud environment, there are various security threats to the patient’s medical data. As a result, security features such as encryption of collected data and access control by legitimate users are essential. Many studies have been conducted on access control techniques using ciphertext-policy attribute-based encryption (CP-ABE), a form of attribute-based encryption, among various security technologies and studies are underway to apply them to the medical field. However, several problems persist. First, as the secret key does not identify the user, the user may maliciously distribute the secret key and such users cannot be tracked. Second, Attribute-Based Encryption (ABE) increases the size of the ciphertext depending on the number of attributes specified. This wastes cloud storage, and computational times are high when users decrypt. Such users must employ outsourcing servers. Third, a verification process is needed to prove that the results computed on the outsourcing server are properly computed. This paper focuses on the IoMT environment for a study of a CP-ABE-based medical data sharing system with key abuse prevention and verifiable outsourcing in a cloud environment. The proposed scheme can protect the privacy of user data stored in a cloud environment in the IoMT field, and if there is a problem with the secret key delegated by the user, it can trace a user who first delegated the key. This can prevent the key abuse problem. In addition, this scheme reduces the user’s burden when decoding ciphertext and calculates accurate results through a server that supports constant-sized ciphertext output and verifiable outsourcing technology. The goal of this paper is to propose a system that enables patients and medical staff to share medical data safely and efficiently in an IoMT environment. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

22 pages, 1688 KiB

Open AccessArticle

A Novel Lightweight Authentication Scheme for RFID-Based Healthcare Systems

by Feng Zhu, Peng Li, He Xu and Ruchuan Wang

Sensors 2020, 20(17), 4846; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174846 - 27 Aug 2020

Cited by 13 | Viewed by 2786

Abstract

The Internet of Things (IoT) has been integrated into legacy healthcare systems for the purpose of improving healthcare processes. As one of the key technologies of IoT, radio frequency identification (RFID) technology has been applied to offer services like patient monitoring, drug administration, and medical asset tracking. However, people have concerns about the security and privacy of RFID-based healthcare systems, which require a proper solution. To solve the problem, recently in 2019, Fan et al. proposed a lightweight RFID authentication scheme in the IEEE Network. They claimed that their scheme can resist various attacks in RFID systems with low implementation cost, and thus is suitable for RFID-based healthcare systems. In this article, our contributions mainly consist of two parts. First, we analyze the security of Fan et al.’s scheme and find out its security vulnerabilities. Second, we propose a novel lightweight authentication scheme to overcome these security weaknesses. The security analysis shows that our scheme can satisfy the necessary security requirements. Besides, the performance evaluation demonstrates that our scheme is of low cost. Thus, our scheme is well-suited for practical RFID-based healthcare systems. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

Review

Jump to: Research

20 pages, 2139 KiB

Open AccessReview

Benefits of Home-Based Solutions for Diagnosis and Treatment of Acute Coronary Syndromes on Health Care Costs: A Systematic Review

by Pau Redón, Atif Shahzad, Talha Iqbal and William Wijns

Sensors 2020, 20(17), 5006; https://0-doi-org.brum.beds.ac.uk/10.3390/s20175006 - 03 Sep 2020

Cited by 2 | Viewed by 3092

Abstract

Diagnosing and treating acute coronary syndromes consumes a significant fraction of the healthcare budget worldwide. The pressure on resources is expected to increase with the continuing rise of cardiovascular disease, other chronic diseases and extended life expectancy, while expenditure is constrained. The objective of this review is to assess if home-based solutions for measuring chemical cardiac biomarkers can mitigate or reduce the continued rise in the costs of ACS treatment. A systematic review was performed considering published literature in several relevant public databases (i.e., PUBMED, Cochrane, Embase and Scopus) focusing on current biomarker practices in high-risk patients, their cost-effectiveness and the clinical evidence and feasibility of implementation. Out of 26,000 references screened, 86 met the inclusion criteria after independent full-text review. Current clinical evidence highlights that home-based solutions implemented in primary and secondary prevention reduce health care costs by earlier diagnosis, improved patient outcomes and quality of life, as well as by avoidance of unnecessary use of resources. Economical evidence suggests their potential to reduce health care costs if the incremental cost-effectiveness ratio or the willingness-to-pay does not surpass £20,000/QALY or €50,000 limit per 20,000 patients, respectively. The cost-effectiveness of these solutions increases when applied to high-risk patients. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures

Figure 1

39 pages, 3192 KiB

Open AccessEditor’s ChoiceReview

Deep Learning in Physiological Signal Data: A Survey

by Beanbonyka Rim, Nak-Jun Sung, Sedong Min and Min Hong

Sensors 2020, 20(4), 969; https://0-doi-org.brum.beds.ac.uk/10.3390/s20040969 - 11 Feb 2020

Cited by 124 | Viewed by 14201

Abstract

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources. Full article

(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)

► Show Figures