Kontrol Lampu Pada Pendeteksian Kantuk dengan Parameter Posisi Tidur Menggunakan Object Detection Effiecientdet Lite
DOI:
https://doi.org/10.30865/mib.v7i4.6466Keywords:
Sleep Detection, Light control, HBI, Raspberry Pi, Object DetectionAbstract
Drowsiness is a sign that the body is entering a phase towards rest. Integrated detection of sleepy activity light control where light control automation is carried out based on the results of object detection, namely humans when they are sleepy, will be needed to efficiently use electrical energy. Analysis of the sleep detection scheme and the effectiveness of the object detection method in detecting sleepiness with body and eye position parameters was carried out using the Efficientdet Lite object detection method as the architecture used in making sleep detection models where the model created is implemented on a Raspberry Pi which is integrated with light control resulting in detection drowsiness with a comparison of 3 sleepiness parameters (body position, eyes, facial expressions) is able to control lights in the form of automatic lights turning off and on based on the labels that appear in each drowsiness detection parameter class. The results obtained were that the metric evaluation produced a different Average Precision for 3 parameters but in the appearance of the bounding box the most precise in detecting objects was the sleeping position, for the eye parameter the bounding box was not appropriate in detecting drowsiness while in facial expressions the bounding box was difficult to appear. Accuracy testing was carried out on the sleep position parameter resulting in a detection accuracy of 80%.
References
D. Krishnan, S. Kelly, and Y. Kim, “A Metaâ€Analysis Review of Occupant Behaviour Models for Assessing Demandâ€Side Energy Consumption,†Energies, vol. 15, no. 3, 2022, doi: 10.3390/en15031219.
Y. Laaroussi, M. Bahrar, M. Elmankibi, A. Draoui, and A. Si-Larbi, “Occupant behaviour: A major issue for building energy performance,†IOP Conf. Ser. Mater. Sci. Eng., vol. 609, no. 7, 2019, doi: 10.1088/1757-899X/609/7/072050.
C. Gao et al., “Sleep Duration/Quality With Health Outcomes: An Umbrella Review of Meta-Analyses of Prospective Studies,†Front. Med., vol. 8, no. January, pp. 1–15, 2022, doi: 10.3389/fmed.2021.813943.
M. N. Kamel Boulos and K. Koh, “Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing,†Int. J. Health Geogr., vol. 20, no. 1, 2021, doi: 10.1186/s12942-021-00266-0.
O. S. Vlasova and F. A. Bichkaeva, “ВозраÑтные Ð˜Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÐŸÐ°Ñ€Ð°Ð¼ÐµÑ‚Ñ€Ð¾Ð² Углеводного Обмена И ОбеÑпеченноÑти Витаминами Ð’1, Ð’2 У Жителей Двух Северных Регионов,†Klin. Lab. Diagn., vol. 66, no. 8, pp. 465–471, 2021, doi: 10.51620/0869-2084-2021-66-8-465-471.
W. L. Hsu et al., “Establishment of smart living environment control system,†Sensors Mater., vol. 32, no. 1, pp. 183–195, 2020, doi: 10.18494/SAM.2020.2581.
A. R. Dhruba, K. N. Alam, M. S. Khan, S. Bourouis, and M. M. Khan, “Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications,†Comput. Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/7152576.
G. Matar, J. M. Lina, J. Carrier, and G. Kaddoum, “Unobtrusive Sleep Monitoring Using Cardiac, Breathing and Movements Activities: An Exhaustive Review,†IEEE Access, vol. 6, no. August, pp. 45129–45152, 2018, doi: 10.1109/ACCESS.2018.2865487.
S. Mehta, S. Dadhich, S. Gumber, and A. J. Bhatt, “Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio Fatigue Detection Non-Intrusive Methods Driver monitoring system,†Proceeding Int. Conf. Sustain. Comput. Sci. Technol. Manag., pp. 1333–1339, 2019, [Online]. Available: https://ssrn.com/abstract=3356401.
A. Zein, “Pendeteksian Kantuk Secara Real Time Menggunakan Pustaka OPENCV dan DLIB PYTHON,†Sainstech J. Penelit. dan Pengkaj. Sains dan Teknol., vol. 28, no. 2, pp. 22–26, 2018, doi: 10.37277/stch.v28i2.238.
V. R. Reddy Chirra, S. R. Uyyala, and V. K. Kishore Kolli, “Deep CNN: A machine learning approach for driver drowsiness detection based on eye state,†Rev. d’Intelligence Artif., vol. 33, no. 6, pp. 461–466, 2019, doi: 10.18280/ria.330609.
R. T. Puteri and P. Utaminingrum, “Deteksi Kantuk Menggunakan Kombinasi Haar Cascade dan Convolutional Neural Network,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 3, pp. 816–821, 2020.
M. Education, “Driver-Drowsiness Detection System,†vol. 12, no. 9, pp. 1892–1898, 2021.
W. Deng and R. Wu, “Real-Time Driver-Drowsiness Detection System Using Facial Features,†IEEE Access, vol. 7, pp. 118727–118738, 2019, doi: 10.1109/ACCESS.2019.2936663.
K. Fujiwara et al., “Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG,†IEEE Trans. Biomed. Eng., vol. 66, no. 6, pp. 1769–1778, 2019, doi: 10.1109/TBME.2018.2879346.
N. Nuryani, P. Sistem Deteksi Kantuk, K. Nisak, and A. Dwijo Sutomo, “Pengembangan Sistem Deteksi Kantuk Menggunakan Pengklasifikasi Random Forest pada Sinyal Elektrokardiogram (Development of Drowsiness Detection System Using Random Forest Classifier on Electrocardiogram Signals),†J. Nas. Tek. Elektro dan Teknol. Inf. |, vol. 10, no. 3, pp. 265–271, 2021.
F. Guede-Fernández, M. Fernández-Chimeno, J. Ramos-Castro, and M. A. GarcÃa-González, “Driver Drowsiness Detection Based on Respiratory Signal Analysis,†IEEE Access, vol. 7, pp. 81826–81838, 2019, doi: 10.1109/ACCESS.2019.2924481.
C. Ticleanu, “Impacts of home lighting on human health,†Light. Res. Technol., vol. 53, no. 5, pp. 453–475, 2021, doi: 10.1177/14771535211021064.
K. Tang, A. Kumar, M. Nadeem, and I. Maaz, “CNN-Based Smart Sleep Posture Recognition System,†Internet of Things, vol. 2, no. 1, pp. 119–139, 2021, doi: 10.3390/iot2010007.
F. Edita and M. Michela, “Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence,†Sleep Breath., pp. 13–24, 2019, [Online]. Available: http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L621796353%0Ahttp://dx.doi.org/10.1007/s11325-018-1661-3.
K. Saleem, I. S. Bajwa, N. Sarwar, W. Anwar, and A. Ashraf, “IoT Healthcare: Design of Smart and Cost-Effective Sleep Quality Monitoring System,†J. Sensors, vol. 2020, 2020, doi: 10.1155/2020/8882378.
Q. Pan, D. Brulin, and E. Campo, “Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review,†JMIR Biomed. Eng., vol. 5, no. 1, p. e20921, 2020, doi: 10.2196/20921.
J. Wang, N. Spicher, J. M. Warnecke, M. Haghi, J. Schwartze, and T. M. Deserno, “Unobtrusive health monitoring in private spaces: The smart home,†Sensors (Switzerland), vol. 21, no. 3, pp. 1–23, 2021, doi: 10.3390/s21030864.
G. B. Dell’isola, E. Cosentini, L. Canale, G. Ficco, and M. Dell’isola, “Noncontact body temperature measurement: Uncertainty evaluation and screening decision rule to prevent the spread of covid-19,†Sensors (Switzerland), vol. 21, no. 2, pp. 1–20, 2021, doi: 10.3390/s21020346.
A. Verma, S. Prakash, V. Srivastava, A. Kumar, and S. C. Mukhopadhyay, “Sensing, Controlling, and IoT Infrastructure in Smart Building: A Review,†IEEE Sens. J., vol. 19, no. 20, pp. 9036–9046, 2019, doi: 10.1109/JSEN.2019.2922409.
M. Tan, R. Pang, and Q. V. Le, “EfficientDet: Scalable and efficient object detection,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 10778–10787, 2020, doi: 10.1109/CVPR42600.2020.01079.
E. Şafak, I. A. Doǧru, N. Barişçi, and S. Toklu, “IoT based mobile driver drowsiness detection using deep learning,†J. Fac. Eng. Archit. Gazi Univ., vol. 37, no. 4, pp. 1869–1881, 2022, doi: 10.17341/gazimmfd.999527.
Ms. Muhammad Abduh Tuasikal, “Nabi Muhammad Tidur Malam Jam Berapa?,†rumaysho.com, 2020. https://rumaysho.com/23193-nabi-muhammad-tidur-malam-jam-berapa.html (accessed Apr. 26, 2022).
S. . dr. Raehanul Bahraen, M.Sc, “Waktu Tidur Ideal Seorang Muslim,†muslim.or.id, 2019. https://muslim.or.id/51745-waktu-tidur-ideal-seorang-muslim.html (accessed Apr. 16, 2022).
Pragati Baheti, “Train Test Validation Split: How To & Best Practices [2023],†v7labs, 2021. https://www.v7labs.com/blog/train-validation-test-set#:~:text=In general%2C putting 80%25 of,dimension of the data%2C etc. (accessed May. 5, 2022).
COCO, “Detection Evaluation,†Common Objects in Contexts, 2022. https://cocodataset.org/#detection-eval.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
