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IOT Medical Applications

IOT Medical Applicationsvon P. Vinoth Kumar Sie sparen 17% des UVP sparen 17%
Über IOT Medical Applications

The fast-modernizing developed and utilized with the help of IoT. Our project work approaches to simplify the patients monitoring by our integrated system of IoT sensors, an emergency diagnostic subsystem a smart health analytic system approaches to secure the patients by automatic monitoring and recording of clinical parameters, and treat them if necessary. The maternal and fetal parameters such as blood pressure, temperature, oxygen level, heart rate are monitored and recorded for those who are hospitalized under high risk condition. The sensors assembled to sense specified parameters record them and the sensed information are recorded and saved in cloud environment. The specialized emergency subsystem developed is used to analyze the data with the threshold levels. The features of the parameters recorded are extracted using CNN. Using deep leaning, the features are classified and studied for further consultations with the specialists. A quick and instant remedies can be sought out in case of emergencies.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9786206769057
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 56
  • Veröffentlicht:
  • 22. September 2023
  • Abmessungen:
  • 150x4x220 mm.
  • Gewicht:
  • 102 g.
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  Versandfertig in 1-2 Wochen.

Beschreibung von IOT Medical Applications

The fast-modernizing developed and utilized with the help of IoT. Our project work approaches to simplify the patients monitoring by our integrated system of IoT sensors, an emergency diagnostic subsystem a smart health analytic system approaches to secure the patients by automatic monitoring and recording of clinical parameters, and treat them if necessary. The maternal and fetal parameters such as blood pressure, temperature, oxygen level, heart rate are monitored and recorded for those who are hospitalized under high risk condition. The sensors assembled to sense specified parameters record them and the sensed information are recorded and saved in cloud environment. The specialized emergency subsystem developed is used to analyze the data with the threshold levels. The features of the parameters recorded are extracted using CNN. Using deep leaning, the features are classified and studied for further consultations with the specialists. A quick and instant remedies can be sought out in case of emergencies.

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