Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition
DOI:
https://doi.org/10.29304/jqcm.2022.14.3.987Keywords:
Object detection,, Deep learning,, Touchless hand sanitizers,, Mobile robot,, DatasetAbstract
The COVID-19 pandemic spreads throughout the world, many sanitizer dispenser devices included robots designed to minimize human contact and as less as possible to minimize the speared of this pandemic or any germs among humans. In many positions, these sanitizers dispensers are important to sterilize and help people from infection such as patients and medical staff in hospitals, teachers, and students in schools or universities, etc. In this paper, many studies of sanitizer dispenser devices including mobile robot applications for touchless hand sanitizers especially in the coronavirus pandemic have been explained. The major aim of this survey is to compare and contrast many previous survey methodologies. This study covered the introduction to robot applications, hand sanitizers dispenser device including a mobile robot, and object detection-based deep learning techniques that recognizes people. Object detection with the deep learning method is discussed because it is the most important feature of the hand sanitizer device, which is based on a mobile robot application. Furthermore, in this work, the proposed block diagram of dataset processing has been instigated using the mini-computer Raspberry Pi version 4, Raspberry Pi camera system, and LabelImg graphical tool. These dataset outputs files of each image (label.xml and image.jpg) are prepared and classed into 7 classes to use as inputs to the next future work of deep learning. In future work, we want to create a mobile robot hand sanitizer that recognizes the people using a deep learning approach. The python programing language has been used. The contribution of this work is to create, annotate and prepare the dataset for Deep Learning while the previous related works depended on existing dataset that taken from website .
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References
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[6] S. Sarker, L. Jamal, S. F. Ahmed, and N. Irtisam, “Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review,” Rob. Auton. Syst., vol. 146, p. 103902, 2021, doi: 10.1016/j.robot.2021.103902.
[7] Paliwal, Mukta, and Usha A. Kumar. "Neural networks and statistical techniques: A review of applications." Expert systems with applications 36.1 (2009): 2-17.
[8] I. G. and Y. B. and A. Courville, Deep learning: A practitioner’s approach, vol. 29, no. 7553. 2017.
[9] S. Khan, H. Rahmani, S. A. A. Shah, and M. Bennamoun, A Guide to Convolutional Neural Networks for Computer Vision, vol. 8, no. 1. 2018.
[10] Youzi Xiao1 · Zhiqiang Tian1, D. · X. Lan2, · Jiachen Yu1 · Yinshu Zhang1 · Shuai Liu1 · Shaoyi, and Received:, A review of object detection based on deep learning, vol. 1684, no. 1. Multimedia Tools and Applications, 2020.
[11] J. Lee, J. Y. Lee, S. M. Cho, K. C. Yoon, Y. J. Kim, and K. G. Kim, “Design of automatic hand sanitizer system compatible with various containers,” Healthc. Inform. Res., vol. 26, no. 3, pp. 243–247, 2020, doi: 10.4258/hir.2020.26.3.243.
[12] P. B. & D. H. Knight, “Robot Vs. Stick: The Impact Of Anthropomorphism On The Use Of Hand Sanitizer,” no. 8.5.2017, pp. 2003–2005, 2021.
[13] Y. Eddy Mohammed, M. N., A. Arif Sameh Al-Zubaidi, S., and A.-S. Omar Ismael Sairah, A. K., “2019 Novel Coronavirus Disease (Covid-19): Design and Development of Disinfectant Fogging System Using IoT Based Drone Technology,” Rev. Argentina Clínica Psicológica, vol. 29, no. 5, p. 221, 2020, doi: 10.24205/03276716.2020.1022.
[14] K. Abuga and N. Nyamweya, “Alcohol-Based Hand Sanitizers in COVID-19 Prevention: A Multidimensional Perspective,” Pharmacy, vol. 9, no. 1, p. 64, 2021, doi: 10.3390/pharmacy9010064.
[15] H. Sathwara, P. Vaghela, and S. Joshi, “AROGYAKAVACHAM—Automatic Hand Sanitizer Dispenser with Temperature Measurement,” no. December, pp. 785–796, 2022, doi: 10.1007/978-981-16-2123-9_61.
[16] E. Edozie, W. Janat, and Z. Kalyankolo, “Design and Implementation of a Smart Hand Sanitizer Dispenser with Door Controller using ATMEGA328P,” Int. J. Eng. Inf. Syst., vol. 4, no. 6, pp. 14–18, 2020, [Online]. Available: www.ijeais.org.
[17] M. M. Srihari, “Self-Activating Sanitizer with Battery Imposed System for Cleansing Hands,” Proc. 2nd Int. Conf. Inven. Res. Comput. Appl. ICIRCA 2020, pp. 1102–1105, 2020, doi: 10.1109/ICIRCA48905.2020.9183347.
[18] Hind. Z. Khaleel, Firas. A. Raheem, and M. Azzam, “Design and Implementation of a Touchless Hand Sanitizer Dispenser Mobile Robot,” vol. 01012, pp. 1–8, 2022.
[19] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
[20] A. R. Nasser et al., “Iot and cloud computing in health-care: A new wearable device and cloud-based deep learning algorithm for monitoring of diabetes,” Electron., vol. 10, no. 21, 2021, doi: 10.3390/electronics10212719.
[21] C. Li, J. Cao, and X. Zhang, “Robust deep learning method to detect face masks,” ACM Int. Conf. Proceeding Ser., no. 1, pp. 74–77, 2020, doi: 10.1145/3421766.3421768.
[22] S. Sachan, “Object Detection using Raspberry Pi,” vol. 6, no. 10, pp. 39–42, 2019.
[23] S. UĞUZ, “Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector,” Sak. Univ. J. Comput. Inf. Sci., vol. 3, no. 3, pp. 158–168, 2020, doi: 10.35377/saucis.03.03.755269.
[24] M. Abagiu, D. Popescu, F. L. Manta, and L. C. Popescu, “Use of a Deep Neural Network for Object Detection in a Mobile Robot Application,” EPE 2020 - Proc. 2020 11th Int. Conf. Expo. Electr. Power Eng., no. Epe, pp. 221–225, 2020, doi: 10.1109/EPE50722.2020.9305648.
[25] M. M. Mahmoud and A. R. Nasser, “Dual Architecture Deep Learning Based Object Detection System for Autonomous Driving,” Iraqi J. Comput. Commun. Control Syst. Eng, vol. 21, no. 2, pp. 36–43, 2021.
[26] W. Liu et al., “SSD: Single shot multibox detector,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016, doi: 10.1007/978-3-319-46448-0_2.
[27] C. Xu and Y. Mao, “An improved traffic congestion monitoring system based on federated learning,” Inf., vol. 11, no. 7, pp. 1–22, 2020, doi: 10.3390/INFO11070365.
[28] I. Ahmed, M. Ahmad, A. Ahmad, and G. Jeon, “IoT-based crowd monitoring system: Using SSD with transfer learning,” Comput. Electr. Eng., vol. 93, no. May, p. 107226, 2021, doi: 10.1016/j.compeleceng.2021.107226.
[29] 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.
[30] G. A. López-Alfaro, L. Á. Hernández-Fernández, J. A. Aguirre-Núñez, J. P. Serrano-Rubio, R. Herrera-Guzmán, and L. M. Rodríguez-Vidal, "Smart IoT Device For Energy Consumption Monitoring In Real Time," in 2021 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2021, vol. 5, pp. 1-6: IEEE.
[31] https://www.raspberrypi.com/. Accessed on 27-3-2022.
[32] https://www.tradeinn.com/techinn/en/raspberry-pi-camera-v2/137489672/p. Accessed on 23-3-2022.
[33] C.-W. Yu, Y.-L. Chen, K.-F. Lee, C.-H. Chen, and C.-Y. Hsiao, "Efficient Intelligent Automatic Image Annotation Method based on Machine Learning Techniques," in 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2019, pp. 1-2: IEEE.
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Published
2022-08-12
How to Cite
Khaleel, R. Z., & Raheem, F. A. (2022). Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 58–65. https://doi.org/10.29304/jqcm.2022.14.3.987
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