The accuracy of Infrared sensor detection in a smart toilet

F1000Res. 2022 Mar 14:10:949. doi: 10.12688/f1000research.73086.2. eCollection 2021.

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°.

Keywords: Infrared sensor; Internet of Things (IoT); Raspberry Pi.

Associated data

  • figshare/10.6084/m9.figshare.16571331

Grants and funding

Telekom Malaysia Research and Development (TMR&D) has funded this project. Grant ID: RDTC/180969