Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm
Abstract
:1. Introduction
1.1. Current Issues Faced by Cleaning Companies
1.2. Solution for Restroom Cleaning Issues
2. Dirtiness Quantification Method
2.1. Definition of Dirtiness
2.2. Overview of Method
3. In Situ Experiment and Results
3.1. Preparation of Water Droplet Images
3.2. Data Collection
3.3. Data Augmentation
- Averaging and scaling method: This approach involved successively taking averages of two nearest neighbouring data points, two second-nearest neighbouring data points, and so on. These averages were then multiplied by 0.9 and 1.1, and the process was repeated. This method generated three sets of augmented data by expanding the margins of the minimum and maximum values.
- Random number generation method: In this method, random numbers were generated between the maximum and minimum values for each dataset.
3.4. Water Droplet Volume Prediction
4. Results and Discussion
4.1. Prediction Using Actual Data
4.2. Setting Hyperparameters of LightGBM
4.3. Differences in Prediction Accuracy with Augmented Data
5. Conclusions
6. Future Study
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Image Sensor | InGaAs |
Number of pixels | 640 × 512 pixels |
Wavelength sensitivity | 0.9–1.7 µm |
Frame rate | 98 fps |
Camera dimensions | 61 × 59 × 81 mm (width × height × depth) (without the lens) |
Sex | Temperature (°C) | Humidity (%) | lag1030 (μL) | lag1130 (μL) | lag1230 (μL) | lag1330 (μL) | lag1430 (μL) | lag1530 (μL) | lag1630 (μL) | lag1730 (μL) |
---|---|---|---|---|---|---|---|---|---|---|
Male | 12.4 | 62.9 | 4.2 | 4.8 | 6.3 | 7.4 | 12.3 | 20.0 | 21.8 | 23.7 |
Male | 16.3 | 82.9 | 1.8 | 1.8 | 1.9 | 12.3 | 20.1 | 22.0 | 25.4 | 30.2 |
Male | 6.6 | 69.6 | 0.0 | 0.2 | 0.4 | 1.6 | 3.1 | 3.1 | 3.1 | 3.9 |
Male | 6.3 | 60.6 | 4.8 | 7.5 | 10.2 | 11.0 | 13.4 | 15.0 | 15.3 | 17.9 |
Male | 8.6 | 48.8 | 7.4 | 9.2 | 11.0 | 12.6 | 12.6 | 13.2 | 13.2 | 17.0 |
Female | 12.4 | 62.9 | 0.5 | 1.0 | 1.6 | 1.9 | 1.9 | 1.9 | 2.6 | 5.0 |
Female | 16.3 | 82.9 | 1.0 | 1.8 | 1.8 | 20.6 | 20.6 | 20.6 | 21.5 | 22.8 |
Female | 6.6 | 69.6 | 7.1 | 10.9 | 14.8 | 14.8 | 17.3 | 17.3 | 17.3 | 17.3 |
Female | 6.3 | 60.6 | 0.5 | 14.7 | 14.8 | 14.8 | 15.3 | 15.9 | 16.0 | 16.3 |
Female | 8.6 | 48.8 | 2.5 | 6.5 | 10.5 | 11.0 | 11.0 | 11.1 | 11.1 | 12.0 |
No. | lag1030 | lag1130 | lag1230 | lag1330 | lag1430 | lag1530 | lag1630 | lag1730 |
---|---|---|---|---|---|---|---|---|
1 | 4.2 | 4.8 | 6.3 | 7.4 | 12.3 | 20.0 | 21.8 | 23.7 |
2 | 1.8 | 1.8 | 1.9 | 12.3 | 20.1 | 22.0 | 25.4 | 30.2 |
3 | 0.0 | 0.2 | 0.4 | 1.6 | 3.1 | 3.1 | 3.1 | 3.9 |
4 | 4.8 | 7.5 | 10.2 | 11.0 | 13.4 | 15.0 | 15.3 | 17.9 |
5 | 7.4 | 9.2 | 11.0 | 12.6 | 12.6 | 13.2 | 13.2 | 17.0 |
6 | 4.5 | 4.6 | 5.5 | 6.0 | 9.1 | 10.6 | 16.5 | 26.2 |
7 | 3.2 | 3.4 | 8.2 | 8.5 | 11.0 | 11.7 | 16.6 | 29.9 |
8 | 4.6 | 5.1 | 5.5 | 5.6 | 7.0 | 11.0 | 12.0 | 15.2 |
9 | 0.3 | 2.6 | 3.9 | 6.4 | 8.9 | 12.8 | 14.8 | 18.9 |
10 | 1.2 | 3.7 | 5.9 | 6.6 | 8.3 | 8.3 | 26.0 | 26.4 |
11 | 0.0 | 0.5 | 8.5 | 8.8 | 14.1 | 18.5 | 21.0 | 21.9 |
12 | 3.2 | 4.8 | 5.1 | 6.8 | 10.1 | 12.6 | 17.7 | 31.3 |
13 | 4.6 | 9.4 | 10.4 | 12.4 | 12.7 | 18.4 | 26.8 | 27.6 |
14 | 1.4 | 1.5 | 2.2 | 9.5 | 12.0 | 16.2 | 16.6 | 21.2 |
15 | 3.4 | 5.7 | 9.9 | 10.9 | 11.5 | 13.9 | 20.6 | 24.0 |
16 | 1.5 | 4.1 | 5.0 | 6.1 | 9.4 | 10.9 | 13.8 | 17.0 |
17 | 5.0 | 5.5 | 6.6 | 7.0 | 9.5 | 9.8 | 17.6 | 22.9 |
18 | 1.9 | 3.4 | 4.8 | 6.0 | 8.9 | 10.1 | 11.0 | 11.8 |
19 | 0.7 | 3.5 | 4.1 | 8.4 | 10.9 | 19.8 | 25.0 | 26.6 |
20 | 0.6 | 3.4 | 8.3 | 8.9 | 10.1 | 14.9 | 15.2 | 32.9 |
21 | 0.9 | 2.7 | 3.8 | 6.8 | 8.5 | 10.0 | 16.6 | 27.7 |
22 | 2.6 | 3.6 | 5.6 | 8.4 | 10.2 | 11.0 | 20.8 | 32.5 |
23 | 3.1 | 3.8 | 4.3 | 7.7 | 11.2 | 20.6 | 21.0 | 21.4 |
24 | 0.5 | 2.3 | 3.4 | 4.0 | 9.1 | 11.1 | 14.8 | 17.5 |
25 | 2.1 | 2.9 | 3.9 | 5.1 | 6.3 | 9.2 | 18.5 | 18.5 |
26 | 2.9 | 3.6 | 9.7 | 11.0 | 13.2 | 16.2 | 20.3 | 25.7 |
27 | 2.3 | 3.8 | 4.4 | 4.4 | 10.5 | 15.8 | 20.7 | 28.1 |
28 | 3.7 | 5.6 | 8.9 | 10.9 | 17.7 | 23.2 | 26.6 | 28.3 |
29 | 3.6 | 6.6 | 7.2 | 8.4 | 10.3 | 11.9 | 13.8 | 32.1 |
30 | 2.6 | 4.8 | 8.1 | 8.3 | 8.3 | 12.4 | 18.9 | 29.1 |
31 | 1.8 | 5.9 | 6.1 | 7.0 | 10.7 | 16.2 | 20.7 | 20.8 |
32 | 4.0 | 4.2 | 5.8 | 6.1 | 7.1 | 10.0 | 11.9 | 23.0 |
33 | 4.1 | 4.3 | 6.0 | 11.7 | 11.8 | 14.1 | 18.1 | 24.6 |
34 | 4.2 | 4.3 | 4.9 | 5.4 | 7.3 | 7.6 | 16.6 | 21.8 |
35 | 0.0 | 5.1 | 8.6 | 10.1 | 10.8 | 11.0 | 17.5 | 22.0 |
36 | 1.7 | 2.5 | 2.8 | 5.5 | 7.8 | 10.1 | 16.6 | 22.6 |
37 | 5.1 | 5.1 | 5.4 | 5.9 | 9.3 | 9.6 | 16.6 | 19.1 |
38 | 1.3 | 3.6 | 4.0 | 4.4 | 6.0 | 9.3 | 9.5 | 13.2 |
39 | 1.6 | 3.7 | 7.0 | 8.4 | 10.1 | 11.0 | 12.6 | 14.3 |
40 | 1.0 | 1.2 | 5.1 | 5.9 | 10.8 | 13.8 | 16.3 | 26.2 |
41 | 0.8 | 1.6 | 3.2 | 3.4 | 3.7 | 8.2 | 11.8 | 11.8 |
42 | 4.4 | 6.6 | 7.9 | 11.8 | 13.1 | 18.8 | 20.2 | 27.1 |
43 | 1.2 | 4.0 | 6.7 | 8.8 | 8.9 | 17.5 | 18.1 | 18.2 |
44 | 6.0 | 6.3 | 6.6 | 7.8 | 11.4 | 12.7 | 15.5 | 17.3 |
45 | 4.3 | 4.4 | 4.9 | 6.4 | 10.5 | 10.7 | 20.1 | 31.3 |
46 | 1.4 | 6.4 | 7.4 | 9.8 | 13.3 | 19.4 | 21.5 | 31.5 |
47 | 2.0 | 2.3 | 2.5 | 6.2 | 7.2 | 22.0 | 25.9 | 27.0 |
48 | 3.9 | 5.6 | 6.8 | 10.2 | 11.3 | 16.7 | 18.5 | 20.2 |
49 | 5.9 | 6.7 | 7.1 | 7.3 | 7.4 | 7.4 | 9.6 | 10.3 |
50 | 5.6 | 6.2 | 8.9 | 10.5 | 12.4 | 13.3 | 16.9 | 20.3 |
51 | 5.5 | 6.0 | 8.0 | 8.3 | 9.9 | 10.9 | 20.4 | 20.6 |
52 | 2.9 | 5.3 | 7.5 | 8.3 | 8.9 | 13.1 | 16.9 | 31.2 |
53 | 0.4 | 6.9 | 7.4 | 12.6 | 13.4 | 20.2 | 20.5 | 25.3 |
54 | 1.1 | 2.7 | 3.3 | 9.0 | 12.7 | 16.0 | 16.8 | 19.0 |
55 | 2.0 | 4.2 | 5.3 | 5.7 | 6.5 | 6.5 | 7.2 | 8.5 |
56 | 0.2 | 2.5 | 2.9 | 4.1 | 7.1 | 7.7 | 8.9 | 15.4 |
57 | 1.7 | 3.2 | 3.8 | 5.2 | 5.2 | 10.2 | 14.3 | 24.0 |
58 | 1.0 | 3.5 | 4.2 | 6.1 | 7.0 | 7.6 | 10.0 | 15.7 |
59 | 2.6 | 5.3 | 6.7 | 8.0 | 8.1 | 10.8 | 14.4 | 27.9 |
60 | 0.7 | 6.3 | 6.5 | 6.9 | 8.7 | 16.4 | 22.6 | 24.7 |
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Kurose, S.; Moriwaki, H.; Matsunaga, T.; Lee, S.-S. Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm. Sensors 2025, 25, 2186. https://doi.org/10.3390/s25072186
Kurose S, Moriwaki H, Matsunaga T, Lee S-S. Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm. Sensors. 2025; 25(7):2186. https://doi.org/10.3390/s25072186
Chicago/Turabian StyleKurose, Sumio, Hironori Moriwaki, Tadao Matsunaga, and Sang-Seok Lee. 2025. "Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm" Sensors 25, no. 7: 2186. https://doi.org/10.3390/s25072186
APA StyleKurose, S., Moriwaki, H., Matsunaga, T., & Lee, S.-S. (2025). Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm. Sensors, 25(7), 2186. https://doi.org/10.3390/s25072186