Abstract
Today’s industrial development has reached the modern digitalization period, where industrial machines can already be linked to the Internet, or what is commonly referred to as the Internet of Things (IoT). As an impact, we no longer need to collect data traditionally because our devices can be linked to software that enables us to collect data in real time. A digital twin represents a virtual process or service that is virtual and enables analysis of the system to identify potential issues before they arise. These technology initiatives boost work productivity and directly collect data from the field, intending to make improvements or changes. Wearable sensors are utilized for the farming environment, crops, and livestock monitoring, maintaining quality, and organizing the food supply chain. More applications can be envisaged, such as monitoring farmers’ movement to prevent hazardous farmer risk activity. Wearable sensors on farmers can be used to create a digital twin control system to help farmers understand their farm performance and improve productivity. Inertial Measurement Unit (IMU) wearable sensors are used to acquire real-time data on bone rotation from a farmer’s body. Components include a microcontroller, sensor, display, and battery. User Datagram Protocol (UDP) communication library sends data and maps it to 3D characters. Based on the farmer’s position, specially trained neural network plays a “cow-milking” animation. This experiment demonstrated the use of wearable sensors to monitor farmer activity in the field. The sensors successfully connect to a farmer character in the game engine-based simulation, and animation is played when the bone rotation value reaches a certain level of wearable sensors that are effective in simulating digital twin control.
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References
Gelernter, D.: Mirror Worlds: Or: The Day Software Puts the Universe in a Shoebox...How It Will Happen and What It Will Mean (NY, 1991; online edn, Oxford Academic 2020). https://doi.org/10.1093/oso/9780195068122.001.0001
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4
Wang, Y., Kang, X., Chen, Z.: A survey of digital twin techniques in smart manufacturing and management of energy applications. Green Energy Intell. Transp. 1(2), 100014 (2022)
Broo, D.G., Bravo-Haro, M., Schooling, J.: Design and implementation of a smart infrastructure digital twin. Autom. Constr. 136, 104171 (2022)
Yu, W., Patros, P., Young, B., Klinac, E., Walmsley, T.G.: Energy digital twin technology for industrial energy management: classification, challenges and future. Renew. Sustain. Energy Rev. 161, 112407 (2022)
Ahmed, I., Ahmad, M., Jeon, G.: Integrating digital twins and deep learning for medical image analysis in the era of COVID-19. Virtual Reality Intell. Hardware 4(4), 292–305 (2022)
Maleki, S., Jazdi, N., Ashtari, B.: Intelligent digital twin in health sector: Realization of a software-service for requirements- and model- based-systems-engineering. IFAC-PapersOnLine, 55(19), 79–84 (2022). 5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies AMEST 2022
Maddahi, Y., Chen, S.: Applications of digital twins in the healthcare industry: case review of an IoT-enabled remote technology in dentistry. Virtual Worlds 1(1), 20–41 (2022)
Verdouw, C., Tekinerdogan, B., Beulens, A., Wolfert, S.: Digital twins in smart farming. Agric. Syst. 189, 103046 (2021)
El Marai, O., Taleb, T., Song, J.S.: Roads infrastructure digital twin: a step toward smarter cities realization. IEEE Netw. 35(2), 136–143 (2021)
Alves, R. G., et al.: A digital twin for smart farming (2019)
Alves, R.G., Maia, R.F., Lima, F.: Development of a digital twin for smart farming: irrigation management system for water saving. J. Clean. Prod. 388, 135920 (2023)
González, J.P., Sanchez-Londoño, D., Barbieri, G.: A monitoring digital twin for services of controlled environment agriculture. IFAC- PapersOnLine, 55(19), 85-90 (2022). 5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies AMEST 2022
Alexopoulos, K., Nikolakis, N., Chryssolouris, G.: Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int. J. Comput. Integr. Manufa. 33(5), 429–439 (2020)
Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018)
Boschert, S., Rosen, R.: Digital twin—the simulation aspect. In: Hehenberger, P., Bradley, D. (eds.) Mechatronic Futures, pp. 59–74. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32156-1_5
Gopal, L., Singh, H., Mounica, P., Mohankumar, N., Challa, N.P., Jayaraman, P.: Digital twin and IOT technology for secure manufacturing systems measurement. Sensors 25, 100661 (2023)
Liu, Y., et al.: A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7, 49088–49101 (2019)
Yin, H., Cao, Y., Marelli, B., Zeng, X., Mason, A.J., Cao, C.: Soil sensors and plant wearables for smart and precision agriculture. Adv. Mater. 33(20), 2007764 (2021)
Neethirajan, S., Tuteja, S.K., Huang, S.T., Kelton, D.: Recent advancement in biosensors technology for animal and livestock health management. Biosens. Bioelectron. 98, 398–407 (2017)
Brophy, K., Davies, S., Olenik, S., Çotur, Y., Ming, D., Van Zalk, N., O’Hare, D., Güder, F., Yetisen, A.K.: The Future of Wearable Technologies. Imperial College London, London (2021)
Schweber, B.: Understanding the RF balun and its transformative function (2015). Accessed 14 Dec 2021. https://www.digikey.com/en/articles/
Esp32. https://www.espressif.com/en/products/socs/esp32,2015-2023. Accessed 14 Dec 2021
Mpu6050 (gyroscope + accelerometer + temperature) sensor module (2023). https://www.electronicwings.com/sensors-modules/mpu6050-gyroscope-accelerometer-temperature-sensor-module. Accessed 8 Dec 2021
Fuller, J.: Ssd1306 \(128\times 64\) mono 0.96 inch i2c oled display (2017). https://datasheethub.com/ssd1306-128x64-mono-0-96-inch-i2c-oled-display/. Accessed 16 Dec 2021
Hosch, W.L.: quaternion (2022). https://www.britannica.com/science/quaternion. Accessed 4 Nov 2022
Chen, X.: Human motion analysis with wearable inertial sensors (2013)
Arduino. Open-source electronic prototyping platform enabling users to create interactive electronic objects (2021). https://www.arduino.cc. Accessed 14 Dec 2021
Jan Kaniewski (Getnamo). Udp-unreal (2018). https://github.com/getnamo/ UDP-Unreal,
Jan Kaniewski (Getnamo). Socketioclient-unreal (2016). https://github.com/getnamo/SocketIOClient-Unreal
BasuMallick, C.: TCP vs. UDP: Understanding 10 key differences (2022). https://www.spiceworks.com/tech/networking/articles/tcp-vs-udp/. Accessed 21 June 2022
Rong, G., Zheng, Y., Sawan, M.: Energy solutions for wearable sensors: a review. Sensors 21(11), 3806 (2021)
Škraba, A., Koložvari, A., Kofjač, D., Stojanović, R., Semenkin, E., Stanovov, V.: Prototype of group heart rate monitoring with ESP32. In: 2019 8th Mediterranean Conference on Embedded Computing (MECO), pp. 1-4. IEEE (2019)
Adobe Systems Incorporated. Mixamo (2023). https://www.mixamo.com/#/. Accessed 6 May 2021
Adhitya, Y., Mulyani, G.S., Köppen, M., Leu, J.S.: IoT and deep learning-based farmer safety system. Sensors 23, 2951 (2023). https://doi.org/10.3390/s23062951
Świtoński, A., Josiński, H., Michalczuk, A., Wojciechowski, K.: Quaternion statistics applied to the classification of motion capture data. Expert Syst. Appl. 164, 113813 (2021). https://doi.org/10.1016/j.eswa.2020.113813
Yang, S., et al.: Learning-based motion-intention prediction for end-point control of upper-limb-assistive robots. Sensors 23, 2998 (2023). https://doi.org/10.3390/s23062998
Hemeren, P., Veto, P., Thill, S., Li, C., Sun, J.: Kinematic-based classification of social gestures and grasping by humans and machine learning techniques. Front. Robot. AI 8, 699505 (2021). https://doi.org/10.3389/frobt.2021.699505
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The authors gratefully acknowledge the support by the Department of Computer Science and Systems Engineering (CSSE), Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Japan.
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Mulyani, G.S., Adhitya, Y., Köppen, M. (2023). Design and Implementation of Farmer Digital Twin Control in Smart Farming. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_49
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