SMS-Based Medical Diagnostic Telemetry Data Transmission Protocol for Medical Sensors

Townsend, Ben; Abawajy, Jemal; Kim, Tai-hoon

doi:10.3390/s110404231

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…in [65]) and telemetry data (e.g. in [66]) become useless if the data is not delivered in a timely fashion. Now, to achieve a high delivery ratio the system has to be energy efficient, and one way to achieve this is by limiting the number of message copies.…”

Section: Discussion and Future Research Issuesmentioning

confidence: 99%

Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey

Soelistijanto

Howarth

2014

IEEE Commun. Surv. Tutorials

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Abstract-Opportunistic networks are a class of mobile ad hoc networks (MANETs) where contacts between mobile nodes occur unpredictably and where a complete end-to-end path between source and destination rarely exists at one time. Two important functions, traditionally provided by the transport layer, are ensuring the reliability of data transmission between source and destination, and ensuring that the network does not become congested with traffic. However, modified versions of TCP that have been proposed to support these functions in MANETs are ineffective in opportunistic networks. In addition, opportunistic networks require different approaches to those adopted in the more common intermittently connected networks, e.g. deep space networks. In this article we capture the state of the art of proposals for transfer reliability and storage congestion control strategies in opportunistic networks. We discuss potential mechanisms for transfer reliability service, i.e. hop-by-hop custody transfer and end-to-end return receipt. We also identify the requirements for storage congestion control and categorise these issues based on the number of message copies distributed in the networks. For single-copy forwarding, storage congestion management and congestion avoidance mechanism are discussed. For multiple-copy forwarding, the principal storage congestion control mechanisms are replication management and drop policy. Finally, we identify open research issues in the field where future research could usefully be focused.

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Section: Discussion and Future Research Issuesmentioning

confidence: 99%

Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey

Soelistijanto

Howarth

2014

IEEE Commun. Surv. Tutorials

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“…e continuous growth of mobile technology has affected every facet of human life around the globe as its support of healthcare objectives through telemedicine, telehealth, and m-health [52] has helped to diagnose and treat patients at low cost especially in the developing countries, where there are limited options of diagnosis and treatment. Out of various communication media available on mobile devices, short messaging service (SMS) has proven to be unique and reliable due to its low cost, reliable delivery, personal to users, and not Internet-oriented service [53,54]. Considering the need to provide good medical care to everyone including rural dwellers with poor electricity and slow Internet connections, it is therefore important to integrate SMS with a medical diagnosis system, thus establishing an SMS-medical diagnosis system to best meet the needs of a common man [55].…”

Section: Introductionmentioning

confidence: 99%

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

Omoregbe

Ndaman

Misra

et al. 2020

Journal of Healthcare Engineering

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The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

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“…A variety of systems have been proposed in the literature that works as remote or telehealth platforms to connect health care workers with medical doctors and to transmit data from the patients to physicians. 16,17 These approaches also incorporate EHR systems but are not focused on epilepsy management, so they do not provide an EEG repository either allow automatic detection of abnormalities in EEG records.…”

Section: Introductionmentioning

confidence: 99%

<p>Intelligent Telehealth System To Support Epilepsy Diagnosis</p>

Molina¹,

Torres²,

Salazar-Cabrera³

et al. 2020

JMDH

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Background: Availability and opportunity of epilepsy diagnostic services is a significant challenge, especially in developing countries with a low number of neurologists. The most commonly used test to diagnose epilepsy is electroencephalogram (EEG). A typical EEG recording lasts for 20 to 30 minutes; however, a specialist requires much more time to read it. Furthermore, no evidence was found in the literature on open-source systems for the costeffective management of patient information using electronic health records (EHR) that adequately integrate EEG analysis for automatic identification of abnormal signals.Objective: To develop an integrated open-source EHR system for the management of the patients' personal, clinical, and EEG data, and for automatic identification of abnormal EEG signals.Methods: The core of the system is an EHR and telehealth service based on the OpenMRS platform. On top of that, we developed an intelligent component to automatically detect abnormal segments of EEG tests using machine learning algorithms, as well as a service to annotate and visualize abnormal segments in EEG signals. Finally, we evaluated the intelligent component and the integrated system using precision, recall, and accuracy metrics. Results: The system allowed to manage patients' information properly, store and manage the EEG tests recorded with a medical EEG device, and to detect abnormal segments of signals with a precision of 85.10%, a recall of 97.16%, and an accuracy of 99.92%. Conclusion: Digital health is a multidisciplinary field of research in which artificial intelligence is playing a significant role in boosting traditional health services. Notably, the developed system could significantly reduce the time a neurologist spends in the reading of an EEG for the diagnosis of epilepsy, saving approximately 65-75% of the time consumed. It can be used in a telehealth environment. In this way, the availability and provision of diagnostic services for epilepsy management could be improved, especially in developing countries where the number of neurologists is low.

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SMS-Based Medical Diagnostic Telemetry Data Transmission Protocol for Medical Sensors

Cited by 10 publications

References 17 publications

Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey

Transfer Reliability and Congestion Control Strategies in Opportunistic Networks: A Survey

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

<p>Intelligent Telehealth System To Support Epilepsy Diagnosis</p>

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