The prototype of infant incubator monitoring system based on the internet of things using NodeMCU ESP8266

Firmansyah, Rifqi; Widodo, Arif; Romadhon, A D; Hudha, M S; Saputra, Pressa Perdana Surya; Lestari, Nurita Apridiana

doi:10.1088/1742-6596/1171/1/012015

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…In general, QualSys scores are high, reflecting good methodological quality, and the individual scores of the two reviewers correlate. Both reviewers rated the methodological quality of one study [40] very poor (0.06 and 0.07), as the technology readiness level of the prototype evaluated in this study was low and the documentation was poor.…”

Section: Characteristics Of Studies and Methodological Qualitymentioning

confidence: 97%

“…Full details can be found in the S1 Data. Twenty-four studies were performed at university medical centres or medical schools; two at technical universities [40,42]. Across all studies, the ages of the patients involved ranged from 24 weeks gestational age to 12 years of age.…”

Section: Characteristics Of Studies and Methodological Qualitymentioning

confidence: 99%

“…Some studies explored novel technologies that made use of self-developed hardware and or software [34,35,40,52,53]. One of these concerned smartphone slow-motion video capture, using available (open source or freeware) photogrammetry and 3D-mesh software to obtain 360 degrees 3D scans [34].…”

Section: Plos Onementioning

confidence: 99%

“…The technique used printed photographs to calculate body length via known dimensions of an object (for example, a table or door) depicted on the same photograph close to the person. One study evaluated a prototype with ultrasonic sensors to measure head circumference inside incubators [40]. Lastly, one study reported a self-developed stereoscopic vision system to measure body length [53] via two digital 2D images, taken from two different viewing angles (Fig 6).…”

Section: Plos Onementioning

confidence: 99%

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Body size measuring techniques enabling stress-free growth monitoring of extreme preterm infants inside incubators: A systematic review

2022

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Introduction Growth monitoring of preterm infants is essential for assessing the nutritional effects on their growth. The current growth monitoring techniques are too stressful, however, for the smallest preterm infants. We performed a systematic review to summarize studies on stress-free techniques for measuring the body size of preterm infants inside incubators other than the traditional calliper and tape measure-based instruments. Methods We searched four online literature databases: Embase, Medline, Web of Science Core Collection, and Cochrane, using search terms related to patients (neonates, infants, children) and body size measuring techniques. By means of expert judgement we assessed the techniques’ suitability for stress-free body size measurement of an infant lying in an incubator. As a criterion for suitability, we used an imaginary ideal technique. Results Twenty-six studies were included in this review. In 24 studies, the technique for body size measurement was related to 3D technology, and the majority of these studies acknowledged clinical superiority of 3D over 2D data. Two 3D techniques were assessed as suitable for stress-free measurement of preterm infants inside incubators. The first technique used a commercially available 3D handheld scanner which needed 3D postprocessing to derive measurement data. The second technique used a self-developed stereoscopic vision system. Conclusions 3D volumetric parameters have higher clinical value for growth monitoring than 2D. In addition, contactless 3D measurements enable stress-free growth monitoring of even the smallest preterm infants. However, the time-consuming 3D postprocessing challenges the usability of 3D techniques. Regrettably, none of the identified suitable 3D techniques met all our requirements of an ideal all-in-one body size measuring technique for extreme preterm infants. Handheld 3D scanning might have the best properties for developing this ideal technique.

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Section: Characteristics Of Studies and Methodological Qualitymentioning

confidence: 97%

Section: Characteristics Of Studies and Methodological Qualitymentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

See 2 more Smart Citations

Body size measuring techniques enabling stress-free growth monitoring of extreme preterm infants inside incubators: A systematic review

2022

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“…In this study, the client is not directly sensing data, data is made in the form of random data based on sensor representation. One of NodeMCU type for prototyping is ESP8266 [28], [29].…”

Section: Methodsmentioning

confidence: 99%

Data delivery performance on MongoDB generated by WSN over high data load

2021

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Wireless sensor networks (WSN) Environment is a collection of nodes that sensing and sending data to other nodes in a large number of nodes. One of the WSN concepts is continuous or periodic data collection, then the data obtained can be further processed. From this process, it is possible to produce large data, so the process of sending and storing data becomes a high load. Data obtained from WSN nodes vary greatly, which affects on sending and storing of data. Therefore, a study is needed to analyze WSN's ability to send and store large amounts of data. Message query telemetry protocol (MQTT) is used because it could save resources on communication and MongoDB used because it does not have the concept of tables and rows, this is very suitable for variations in data generated by WSN. In this study, it can be concluded that the performance of MongoDB on high data amount is acceptable, so MongoDB is highly recommended for WSN implementation.

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