Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning

Rahman Indra Kesuma(1*), Rivaldo Fernandes(2), Martin Clinton Tosima Manullang(3),

(1) Institut Teknologi Sumatera
(2) Institut Teknologi Sumatera
(3) Institut Teknologi Sumatera
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v8i1.15205

Abstract

A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing health protocols and adapting a new normal lifestyle. Implementation of the health protocol creates new problems, especially in the health check at the main entrance. The officers in charge of measuring body temperature are at risk of getting infected by COVID. Such a measurement is prone to errors. This study proposed a solution to build an automatic gate system that worked based on the new normal health protocol. The system utilizes the MLX90614 contactless temperature sensor to probe body temperature. It applies deep learning implementing the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture as a determinant of the conditions of wearing face masks. The system is equipped with an IoT-based remote controller to control the gate. Experimental results prove that the system works well. Temperature measurement takes a response time of 20 seconds for each user with 99% accuracy for the sensor and masks classification model.

Keywords

COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614;

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References

R. Q. Cron and W. W. Chatham, “The Rheumatologist’s Role in COVID-19,” J. Rheumatol., vol. 47, no. 5, pp. 639–642, 2020, doi: 10.3899/jrheum.200334.

Kementrian Kesehatan RI, “Info Infeksi Emerging Kementrian Kesehatan RI,” infeksiemerging.kemkes.go.id, 2020. https://infeksiemerging.kemkes.go.id/ (accessed Jun. 21, 2020).

Institut Teknologi Sumatera, “ITERA Mulai Terapkan New Normal Kehidupan Kampus,” itera.ac.id, 2020. https://www.itera.ac.id/itera-mulai-terapkan-new-normal-kehidupan-kampus/ (accessed Jun. 21, 2020).

A. S. Oluwole, T. Adefarati, K. Olusuyi, A. Babarinde, and E. Hilary, “Design of Automatic Gate Control Using Infrared Remote With Password Protected,” Int. J. Res. Dev. Technol., vol. 2, no. 5, pp. 6–12, 2014.

N. Dileep and S. Shanthi, “Automatic Gate using Face Recognition Technique using HAAR Cascade Algorithm,” Int. J. Eng. Adv. Technol., vol. 9, no. 3, pp. 1302–1305, 2020, doi: 10.35940/ijeat.c5195.029320.

H. F. Tang and K. Hung, “Design of a non-contact body temperature measurement system for smart campus,” 2016 IEEE Int. Conf. Consum. Electron. ICCE-China 2016, pp. 0–3, 2017, doi: 10.1109/ICCE-China.2016.7849773.

M. Jiang, X. Fan, and H. Yan, “RetinaMask: A Face Mask detector,” 2020, [Online]. Available: http://arxiv.org/abs/2005.03950.

N. H. Wijaya, Z. Oktavihandani, K. Kunal, E. T. Helmy, and P. T. Nguyen, “Tympani thermometer design using passive infrared sensor,” J. Robot. Control, vol. 1, no. 1, pp. 27–30, 2020, doi: 10.18196/jrc.1106.

S. Alyamkin et al., “Low-Power Computer Vision: Status, Challenges, and Opportunities,” IEEE J. Emerg. Sel. Top. Circuits Syst., vol. 9, no. 2, pp. 411–421, 2019, doi: 10.1109/JETCAS.2019.2911899.

M. Sonka, V. Hlavac, and R. Boyle, Image processing, Analysis, and Machine Vision-Cengage Learning. 2014.

J. E. Hall and A. C. Guyton, Guyton and Hall Textbook of Medical Physiology, 12th ed. Jackson, 2011.

S. Bi, Y. Zhang, M. Dong, and H. Min, “An embedded inference framework for convolutional neural network applications,” IEEE Access, vol. 7, pp. 171084–171094, 2019, doi: 10.1109/ACCESS.2019.2956080.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510–4520, 2018, doi: 10.1109/CVPR.2018.00474.

J. Sigut, M. Castro, R. Arnay, and M. Sigut, “OpenCV Basics: A Mobile Application to Support the Teaching of Computer Vision Concepts,” IEEE Trans. Educ., vol. 63, no. 4, pp. 328–335, 2020, doi: 10.1109/TE.2020.2993013.

A. Kaehler and G. Bradski, OpenCV 3. 2016.

M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Nov. 2016, pp. 265–283, [Online]. Available: https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi.

O. Gurav, “Face Mask Detection Dataset,” 2020. https://www.kaggle.com/omkargurav/face-mask-dataset (accessed Jun. 26, 2020).

D. Makwana, “Face Mask Classification,” www.kaggle.com, 2020. https://www.kaggle.com/dhruvmak/face-mask-detection (accessed Jun. 21, 2020).

Sumansid, “FaceMask Dataset,” 2020. https://www.kaggle.com/sumansid/facemask-dataset (accessed Jun. 21, 2020).

E. Ayan and H. M. Ünver, “Data augmentation importance for classification of skin lesions via deep learning,” in 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), 2018, pp. 1–4, doi: 10.1109/EBBT.2018.8391469.

A. Kasagi, T. Tabaru, and H. Tamura, “Fast algorithm using summed area tables with unified layer performing convolution and average pooling,” in 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, pp. 1–6, doi: 10.1109/MLSP.2017.8168154.

T. Y. Hsiao, Y. C. Chang, H. H. Chou, and C. Te Chiu, “Filter-based deep-compression with global average pooling for convolutional networks,” J. Syst. Archit., vol. 95, no. June 2018, pp. 9–18, 2019, doi: 10.1016/j.sysarc.2019.02.008.

S. Dittmer, E. J. King, and P. Maass, “Singular Values for ReLU Layers,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 9, pp. 3594–3605, 2020, doi: 10.1109/TNNLS.2019.2945113.

H. Ide and T. Kurita, “Improvement of learning for CNN with ReLU activation by sparse regularization,” Proc. Int. Jt. Conf. Neural Networks, vol. 2017-May, pp. 2684–2691, 2017, doi: 10.1109/IJCNN.2017.7966185.

Y. Zhang, E. Zhang, and W. Chen, “Deep neural network for halftone image classification based on sparse auto-encoder,” Eng. Appl. Artif. Intell., vol. 50, pp. 245–255, 2016, doi: 10.1016/j.engappai.2016.01.032.

M. Wang, S. Lu, D. Zhu, J. Lin, and Z. Wang, “A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning,” 2018 IEEE Asia Pacific Conf. Circuits Syst. APCCAS 2018, pp. 223–226, 2019, doi: 10.1109/APCCAS.2018.8605654.

D. Zhu, S. Lu, M. Wang, J. Lin, and Z. Wang, “Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 12, pp. 3382–3386, 2020, doi: 10.1109/TCSII.2020.3002564.

E. Kristiani, C. T. Yang, and C. Y. Huang, “ISEC: An Optimized Deep Learning Model for Image Classification on Edge Computing,” IEEE Access, vol. 8, pp. 27267–27276, 2020, doi: 10.1109/ACCESS.2020.2971566.

J. Sepúlveda and S. A. Velastín, “F1 score assesment of Gaussian mixture background subtraction algorithms using the MuHAVi dataset,” IET Semin. Dig., vol. 2015, no. 5, pp. 1–6, 2015, doi: 10.1049/ic.2015.0106.

L. Dagne, “Flutter for Cross-Platform App and SDK Development,” Metrop. Univ. Appl. Sci., no. May, 2019, [Online]. Available: https://www.theseus.fi/bitstream/handle/10024/172866/Lukas Dagne Thesis.pdf?sequence=2&isAllowed=y.

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