Respiratory rate measurement in children using a thermal camera

Abstract— Respiratory rate is a vital physiological measurement used in the immediate assessment of unwell children. Con-venient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although de-vices which measure respiratory rate exist, none has entered everyday clinical practice. An accurate device which has no physical contact with the child is important to ensure readings are not affected by distress. A thermal imaging camera to moni-tor respiratory rate in children was evaluated. Facial thermal images of 20 children (age: median=6.5 years, range 6 months-17 years) were included in the study. Record-ings were performed while the children slept comfortably on a bed for a duration of two minutes. Values obtained using the thermal imaging camera were compared with those obtained from standard methods: nasal thermistor, respiratory impedance plethysmography and transcutaneous CO2. Median respiratory rate measurements per minute were 21.0 (range 15.5-34.0) using thermal imaging and 19.0 (range 15.3-34.0) using standard methods. A close correlation (r 2 = 0.994) was observed between the thermal imaging and the standard methods. The thermal imaging camera is an accurate, objective non-invasive device which can be used to measure respiratory rate in children

Identification Based on Iris Detection Technique

Iris-biometrics are an alternative way of authenticating and identifying a person because biometric identifiers are unique to people. This paper introduces a method aims to efficient human identification by enhanced iris detection method within acceptable time. After preparing various type of images, then perform a series of pre-processing steps and standardize them, after that use Uni-Net learning, so identify the human by Navie-Bays method is the last step based on the output of Uni-Net which is role as feature extractor for the iris part and another sub-net for non-iris part that may involve identification-outcome. The outcome of this method looked good compared to some high-level methods, so, was accuracy-rate 9855, 99.25, and 99.81 for CASIA-v4, ITT-Delhi, and MMU-database respectively. Also, this paper introduces a method of iris recognition using CNN model which is improved the preprocessed patterns that together from dataset applied some procedures to develop them based on techniques of equalization and acclimate contrast ones. After that characteristic extracted and classified using CNN that comprises of 10 layers with back-propagation schema and adjusted moment evaluation Adam-optimizer for modernize weights. The overall accuracy was 95.31% with utilization time 17.58 (mints) for training-model