Abstract
Due to an ongoing epidemic, the number of hospitalized bedridden patients has increased. It is imperative to closely monitor the hospital room and maintain and clean it regularly to avoid harm to the patients. The accommodations may not be within the standards for a bed-bound patient’s room. The patient has a risk of contracting a respiratory disease, or having an asthmatic attack, if exposed to high levels of PM1, PM2.5, or PM10 dust that cannot be seen with bare eyes so the risk factor is not easy to notice. The goal of this study was to develop a dust monitoring system for hospital bedrooms using IoT, so that the caregivers can monitor air quality in the room. By applying the Internet of Things (IoTs) technology to communicate between sensors and mobile phones, the internet serves as the medium for communication. The demonstration system in the room was equipped with 5 sensor cluster, each measuring: temperature, humidity and dust sensors for PM1, PM2.5, and PM10. Decision trees were trained to predict the outcome of cases after collecting data. The final decision tree model reached an overall classification accuracy of 92.8%. The system could alert for housekeeping or turn on or off an automatic dust remover based on the amount of dust in the room. It also supports cleaning and dust removal to ensure that the bed patient’s room is appropriate and reduces the risk of respiratory diseases caused by dust.
Highlights
In Thailand, there is an increasing trend in ambient airborne particulate matter (PM) for example in the below 2.5 microns size range
The PM, humidity and temperature levels at each position in the room were displayed in real time
Intelligent monitoring From temperature, humidity and dust measurements in the room, the study collected time series data with 8 fields of various inputs
Summary
In Thailand, there is an increasing trend in ambient airborne particulate matter (PM) for example in the below 2.5 microns size range. There are a variety of human health problems from exposure to PM 2.5 dust (e.g. nonfatal heart attacks, aggravated asthma, allergies, eye irritation etc.). According to statistical data from the Department of Health in 2019, an estimated 232,876 people were considered confined patients (homebound elderly or bedridden patients), and the annual numbers are on the increase in Thailand [5]. With the rapid increase in bedridden patients in developing countries, the characteristics of the lodging provided affect the patient’s life. The bedridden patients cannot exercise, move about, or find a more pleasant environment. The bedridden patients need a safe environment in their room, and automation can contribute in such assisted living
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