3 research outputs found
Internet of medical things – integrated, ultrasound-based respiration monitoring system for incubators
The study's aim was to develop a non-contact, ultrasound (US) based respiration rate
and respiratory signal monitor suitable for babies in incubators. Respiration rate
indicates average number of breaths per minute and is higher in young children than
adults. It is an important indicator of health deterioration in critically ill patients. The
current incubators do not have an integrated respiration monitor due to complexities in
its adaptation. Monitoring respiratory signal assists in diagnosing respiration rated
problems such as central Apnoea that can affect infants. US sensors are suitable for
integration into incubators as US is a harmless and cost-effective technology.
US beam is focused on the chest or abdomen. Chest or abdomen movements, caused
by respiration process, result in variations in their distance to the US transceiver located
at a distance of about 0.5 m. These variations are recorded by measuring the time of
flight from transmitting the signal and its reflection from the monitored surface.
Measurement of this delay over a time interval enables a respiration signal to be
produced from which respiration rate and pauses in breathing are determined.
To assess the accuracy of the developed device, a platform with a moving surface was
devised. The magnitude and frequency of its surface movement were accurately
controlled by its signal generator. The US sensor was mounted above this surface at a
distance of 0.5 m. This US signal was wirelessly transmitted to a microprocessor board
to digitise. The recorded signal that simulated a respiratory signal was subsequently
stored and displayed on a computer or an LCD screen. The results showed that US could
be used to measure respiration rate accurately. To cater for possible movement of the
infant in the incubator, four US sensors were adapted. These monitored the movements
from different angles. An algorithm to interpret the output from the four US sensors
was devised and evaluated. The algorithm interpreted which US sensor best detected
the chest movements.
An IoMT system was devised that incorporated NodeMcu to capture signals from the
US sensor. The detected data were transmitted to the ThingSpeak channel and
processed in real-time by ThingSpeak’s add-on Matlab© feature. The data were
processed on the cloud and then the results were displayed in real-time on a computer
screen. The respiration rate and respiration signal could be observed remotely on
portable devices e.g. mobile phones and tablets. These features allow caretakers to have
access to the data at any time and be alerted to respiratory complications.
A method to interpret the recorded US signals to determine respiration patterns, e.g.
intermittent pauses, were implemented by utilising Matlab© and ThingSpeak Server.
The method successfully detected respiratory pauses by identifying lack of chest
movements. The approach can be useful in diagnosing central apnoea. In central apnoea,
respiratory pauses are accompanied by cessation of chest or abdominal movements. The
devised system will require clinical trials and integration into an incubator by
conforming to the medical devices directives. The study demonstrated the integration
of IoMT-US for measuring respiration rate and respiratory signal. The US produced
respiration rate readings compared well with the actual signal generator's settings of the
platform that simulated chest movements
Respiration Measurement in a Simulated Setting Incorporating the Internet of Things
The Internet of Things (IoT) in healthcare has gained significant attention in recent years. This study demonstrates an adaptation of IoT in healthcare by illustrating a method of respiration rate measurement from a platform that simulates breathing. Respiration rate is a crucial physiological measure in monitoring critically ill patients. The devised approach, with further development, may be suitable for integration into neonatal intensive care units (NICUs) to measure infants’ respiration rate. A potential advantage of this method is that it monitors respiration using a wireless non-contact method and could add benefits such as preservation of skin integrity. The paper aimed to assess the accuracy of an IoT-integrated ultrasound (US)-based method for measuring respiration rate. Chest movement due to respiration was simulated by a platform with a controllable moving surface. The magnitude and frequency of the movements were accurately controlled by a signal generator. The surface movements were tracked using US as a reliable and cost-effective technology. ESP8266 NodeMCU was used to wirelessly record the US signal and ThingSpeak and Matlab© were used to analyze and visualize the data in the cloud. A close relationship between the measured rate of the simulated respiration and the actual frequency was observed. The study demonstrated a possible adaption of IoT for respiration rate measurement, however further work will be needed to ensure security and reliability of data handling before use of the system in medical environments
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Identifying Conservation Priorities for a Widespread Dugong Population in the Red Sea: Megaherbivore Grazing Patterns Inform Management Planning
Extensive home ranges of marine megafauna present a challenge for systematic conservation
planning because they exceed spatial scales of conventional management. For elusive species
like dugongs, their management is additionally hampered by a paucity of basic distributional
information across much of their range. The Red Sea is home to a wide-spread, globally important but data-poor population of dugongs. We surveyed the north-eastern Red Sea in the
waters of NEOM, Kingdom of Saudi Arabia, to locate feeding sites and determine priority
areas for dugong conservation. We conducted large-scale in-water surveys of dugong feeding
trails across 27 seagrass meadows that span 0.7 degree of latitude and recorded nine seagrass
species and 13 dugong feeding sites. Spread over ~ 4‚061 km2 of nearshore and offshore
waters, many of these sites clustered around five main core feeding areas. Dugong feeding
trails were mostly recorded at sites dominated by the fast-growing pioneer seagrasses
Halodule uninervis, Halophila ovalis and/or H. stipulacea. Multispecific meadows with
pioneer seagrasses tended to be sheltered and shallow, reflecting a similar spatial pattern to
the identified dugong feeding sites. Often close to hotels and fishing harbours, these high-use
dugong areas are subject to high boat traffic, fishing, and coastal development which places
considerable pressures on this vulnerable mammal and its seagrass habitat. The rapidly
accelerating coastal development in the northern Red Sea directly threatens the future of its
dugong population. Although our sampling focuses on feeding signs in early successional
seagrasses, the results are valuable to spatial conservation planning as they will trigger
overdue conservation interventions for a globally threatened species in a data-poor area.
Urgent dugong conservation management actions in the northern Red Sea should focus on
shallow waters sheltered by coastal lagoons, bays and the lee of large islands.Peer reviewe