RIPPLE: Scalable medical telemetry system for supporting combat rescue

Renner, Adam; Williams, Robert L.; McCartney, Matthew; Harmon, Brandon; Boswell, Lucas; Ganapathy, Subhashini; Abhyankar, Kushal; West, Jason; Weiner, Nir; Weinle, Nathan

doi:10.1109/naecon.2014.7045807

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The screening process of the telemedicine services in Italy was performed between 2008 and 2010 by selecting a database generated by the Italian Ministry of Health. It helps to find the different cases under the telemedicine services and the factors which can be taken care of for making effective telemedicine services [41]. The studies found that organizational stability and financial condition for telemedicine services play a crucial role.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Internet of Things‐Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning

Kumar¹,

Pullagura

Kumari³

et al. 2022

Wireless Communications and Mobile Computing

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It is becoming more popular because of the widespread availability of high-speed Internet and the widespread use of mobile phones, both of which are contributing to its rise in popularity. One such significant issue is the use of mobile phones by working parents to keep an eye on the behaviour of their children while they are babysitting for others, which is a significant concern. This study introduces the notion of a smart cradle, which enables for this kind of video monitoring to be carried out on an infant. An infant’s scream triggers the automated swinging of this cradle, which begins when the sensor detects it. In addition, if the baby’s cry persists for an extended period of time, the gadget triggers a buzzer and sends a text message to the phone, signalling that the cradle is no longer capable of handling the infant and that the baby needs human assistance if the cradle’s mattress is wet. This cradle is equipped with an automatic spinning toy for the baby’s entertainment, which reduces the likelihood of a newborn crying throughout the day. A notable rise in the number of working mothers has been seen in recent years. Consequently, the vast majority of parents place their children in the care of their grandparents or in the care of child-minding companies. In addition, we developed a new algorithm for our system, which is critical in delivering better newborn care when parents are not there to assist. Due to the fact that parents are unable to continuously monitor their children’s condition in ordinary or atypical situations while they are at work, this is the case. Node is the fundamental building unit of a microcontroller. Over the course of a single day, the controller board will collect data from the sensors and transmit it to the Adafruit MQTT server over Wi-Fi to be processed. In addition to monitoring the baby’s vital signs, sensors are also used to detect environmental conditions such as the temperature, humidity, and amount of crying in the surroundings. Using the NX Siemens software, a prototype for the cradle was created. Red meranti wood was utilized in its construction. Whenever the baby begins to scream, the system design automatically swings the infant cradle, which is powered by a motor that is built into the system. In addition, parents may use an external web camera to keep checks on the health of their children when they are away from home. Every aspect of this network has been tested to guarantee that it functions effectively and safely at all times.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Internet of Things‐Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning

Kumar¹,

Pullagura

Kumari³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Another author [79] presented a diorama-based system for urban environments in indoor/outdoor settings and for triage in areas where radio-frequency identification is used for the locations and points of interest of patients to promote awareness on operations, such as search and rescue. In a ripple project [80], a medical body area network of sensors is generated to triage patients in adverse circumstances, such as disasters. Other authors, such as those of a previous study [63], presented a multisource healthcare architecture framework for designing telemonitoring systems within integrated multiple sources and sending messages via an adaptive data fusion method.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of IoT-based mhealth framework for various cases of heart disease patients

et al. 2021

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A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/ outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

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“…A diorama-based system was presented in [72] to enable awareness searching and rescue operations in urban environments in indoor/outdoor settings and for triaging patients by RFID to mark the patients' locations and points of interest. A ripple project presented in [73] involved the creation of a medical body area network of sensors for the triaging process in disaster scenarios.…”

Section: A: Triage-based Disaster Casualties and Incidentsmentioning

confidence: 99%

Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

et al. 2019

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The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient's emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the ''healthcare services package/TAH'' of each hospital and the ''hospital list''. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.INDEX TERMS Telemedicine, Internet of thing, healthcare, triage, hospital selection, medical centre failure.

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RIPPLE: Scalable medical telemetry system for supporting combat rescue

Cited by 5 publications

References 5 publications

[Retracted] Internet of Things‐Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning

[Retracted] Internet of Things‐Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning

Development of IoT-based mhealth framework for various cases of heart disease patients

Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

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