No abstract

Background: Infrared (IR) sensors are useful tools for detecting distance and proximity. However, these sensors are not good at detecting edges of an area, therefore when used in a smart toilet it has difficulty in detecting the orientation and position of the user’s body. The aim of this study was to design an IR sensor for a smart toilet with a more accurate and consistent detection.  Methods: A total of 12(six men and six women) participants with different body types were involved in this study. IR sensor detection was tested in the sitting and squatting toilets. For the best accuracy, the  IR sensor's angle was measured. Red, blue, and red-blue plastic covers were used, as these colors improve precision. The microcontroller was  set up to calculate the participant’s distance and presence in the cubicle.    Results:  Toilet positioning varied greatly depending on whether one is sitting or squatting. For sitting toilet, the red cover was close to the accurate distance at a 172˚ angle. IR detected a man but not a woman's body. The blue cover provided the same best angle of 172˚ with a higher sensor distance. When the red and blue cover combination was applied, the reading of 141cm detected both men and women, at 172˚  angle. The actual distance for squatting toilets  was 158cm. The optimal angle for both red and blue covers was 176˚, however the sensor distance was greater for the blue cover. Finally, the red and blue cover combination gave a more accurate distance of up to 163cm from the actual reading, when detecting both genders at a normal angle of 76˚.  Conclusion: The combination of red and blue cover gave the most accurate detection for the squatting and sitting toilets. The best angle for sitting was 172˚, and for squatting was 176˚.

Modern society needs bathrooms. Poor sanitation is caused by worn-out appliances and expensive cleaning. The technique also requires an inexpensive, dependable sensor. This study had three goals. Creating an IoT administration platform is the main goal. Literature evaluations assess the merits and downsides of existing systems. Second, we suggest predictive maintenance to assist predict bathroom equipment breakdowns. Finally, a scheduling algorithm was used to determine how many janitors to hire. We'll measure the model's effectiveness and make future recommendations. Infrared, temperature and humidity sensors create an IoT bathroom. Sensors have been studied to understand how to adapt them to the hygienic and private toilet environment. Sensor accuracy and cost-effectiveness could be enhanced with more development and testing. The Auto-Regressive Integrated Moving Average (ARIMA) model accurately predicts time series lags, making it a good candidate for predictive maintenance. Long Short-Term Memory (LSTM) is good in time series predictions, therefore it's fair to compare the two. We use the ARIMA model to handle Remaining Useful Life (RUL) prediction techniques by altering Moving Average (MA) and Auto-Regressive (AR). A genetic algorithm is used to create a janitorial cleaning schedule. The genetic algorithm was proposed to schedule cleaning workers. This approach improves the genetic algorithm by studying soft and hard scheduling restrictions. The Greedy algorithm is used to compare. Experimental evaluations reveal that the suggested model ARIGA meets both goals.

Background: Infrared (IR) sensors are useful tools for detecting distance and proximity. However, these sensors are not good at detecting edges of an area, therefore when used in a smart toilet it has difficulty in detecting the orientation and position of the user’s body. The aim of this study was to design an IR sensor for a smart toilet with a more accurate and consistent detection.  Methods: A total of 12(six men and six women) participants with different body types were involved in this study. IR sensor detection was tested in the sitting and squatting toilets. For the best accuracy, the  IR sensor's angle was measured. Red, blue, and red-blue plastic covers were used, as these colors improve precision. The microcontroller was  set up to calculate the participant’s distance and presence in the cubicle.    Results:  Toilet positioning varied greatly depending on whether one is sitting or squatting. For sitting toilet, the red cover was close to the accurate distance at a 172˚ angle. IR detected a man but not a woman's body. The blue cover provided the same best angle of 172˚ with a higher sensor distance. When the red and blue cover combination was applied, the reading of 141cm detected both men and women, at 172˚  angle. The actual distance for squatting toilets  was 158cm. The optimal angle for both red and blue covers was 176˚, however the sensor distance was greater for the blue cover. Finally, the red and blue cover combination gave a more accurate distance of up to 163cm from the actual reading, when detecting both genders at a normal angle of 76˚.  Conclusion: The combination of red and blue cover gave the most accurate detection for the squatting and sitting toilets. The best angle for sitting was 172˚, and for squatting was 176˚.