Respiration Measurement in a Simulated Setting Incorporating the Internet of Things
1.1. The IoT in Healthcare
- Application layer: account hijacking, ransomware and brute force
- Middleware layer: cross-sire request forgery and scripting, session hijacking
- Network layer: eavesdropping, replay, access, denial of service
- Perception layer: device tampering
1.2. Respiration Rate
2. IoT for the Neonatal Intensive Care Unit (NICU)
3. Set-Up for the Simulated Respiration Rate Measurement
4. Integration of IoT into the Simulated Respiration Rate Measurement System
- Receiving the US signal reflected from the surface being monitored.
- Real-time wireless US data transmission to the ThingSpeak server.
- Data aggregation and analysis that included respiration rate calculation through the ThingSpeak server.
- Displaying the results through the ThingSpeak server with the aid of integrated Matlab© software.
- US transceiver—The type of US transceiver was HC-SR04. This is a 4-pin module, whose pin names are supply voltage (Vcc), trigger, echo and ground. It provides 2 to 400 cm noncontact distance measurement. Its distance measurement accuracy can be 3 mm. The module contains US transmitter, receiver and control circuits. It operates by automatically sending eight 40 kHz signals for its measurements.
- ESP8266 NodeMCU—This is an IoT device that connects to an active wireless network. It was used to record the US signal and wirelessly transmit it to the ThingSpeak for processing over the cloud. NodeMCU connects an active wireless network for data transmission to the ThingSpeak. It is an open-source IoT platform. It has a firmware which runs on ESP8266, an integrated Wi-Fi chip, a power amplifier, power management modules, antenna switches and filters.
- ThingSpeak—This is an open-source web-based IoT analytics platform that allows users to aggregate data, analyzes them using web-integrated Matlab© and visualizes live data streams in the cloud. It allows users to create channels like YouTube or television . The data can be analyzed and visualized using different ThingSpeak applications based on packages such as Matlab©. Matlab© was used for data analysis in this study. ThingSpeak has designed a secured system for user data protection. Each ThingSpeak channel has its own unique and secure read and write Application Programming Interface (API) keys.
5. Results and Discussion
- Accuracy of the frequency setting on the signal generator.
- Accuracy of manually reading the frequency associated with the peak in the magnitude frequency spectrum.
- Level of accuracy provided by the US transceiver.
- Tracking: The US-based RR measurement method works while the baby’s chest (or abdomen if moving due to respiration) remains within the US field of view. If the baby moves, repositioning of the US transceiver will be needed. This repositioning requires the position of the baby to be tracked. A number of tracking algorithms were reported that may be considered. A relatively simple tracking approach is template matching. In this approach, the template of the section being tracked is segmented from the first image in the video and correlation is used to identify the section in the following images . Another approach is the Kanade-Lucas-Tomasi (KLT) feature tracker  that looks for unique feature points in a region of interest and then tracks these points in subsequent images. The tracking algorithm requires a camera. An infrared thermal camera allows the baby to be viewed without reliance on background light, but it is more expensive than a visual camera.
- Incorporation into neonatal incubators: This requires design considerations that encompass the infants, medical practitioners and family members .
- Integration into smart hospital infrastructure: This will allow clinicians to be alerted if an anomaly in the child’s breathing is detected through warning messages sent to the clinicians’ digital devices .
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
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|Signal Generator Frequency, Hz, (S)||Ultrasound Signal Frequency, Hz, (U)||S-U|
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Abdulqader, T.; Saatchi, R.; Elphick, H. Respiration Measurement in a Simulated Setting Incorporating the Internet of Things. Technologies 2021, 9, 30. https://doi.org/10.3390/technologies9020030
Abdulqader T, Saatchi R, Elphick H. Respiration Measurement in a Simulated Setting Incorporating the Internet of Things. Technologies. 2021; 9(2):30. https://doi.org/10.3390/technologies9020030Chicago/Turabian Style
Abdulqader, Tareq, Reza Saatchi, and Heather Elphick. 2021. "Respiration Measurement in a Simulated Setting Incorporating the Internet of Things" Technologies 9, no. 2: 30. https://doi.org/10.3390/technologies9020030