Abstract
Over the last decades, number of embedded and portable computer systems for monitoring of activities of miners and underground environmental conditions that have been developed has increased. However, their potential in terms of computing power and analytic capabilities is still underestimated. In this paper we elaborate on the recent examples of the use of wearable devices in mining industry. We identify challenges for high level monitoring of mining personnel with the use of mobile and wearable devices. To address some of them, we propose solutions based on our recent works, including context-aware data acquisition framework, physiological data acquisition from wearables, methods for incomplete and imprecise data handling, intelligent data processing and reasoning module, hybrid localization using semantic maps, and adaptive power management. We provide a basic use case to demonstrate the usefulness of this approach.
Supported by the AGH University grant.
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References
The smart cup. EdanSafe Pty Ltd. (2017). http://smartcaptech.com/pdf/SmartCapFAQB-2.pdf
The smart helmet. Mining World (2017). http://miningworld.com/index.php/2017/09/20/the-smart-helmet/
Statistics of dangerous events occurrence and accidents in mines in years 2015–2017. State Mining Authority, Poland, Katowice (2018). http://www.wug.gov.pl/bhp
Akyildiz, I.F., Stuntebeck, E.P.: Wireless underground sensor networks: research challenges. Ad Hoc Netw. 4(6), 669–686 (2006). https://doi.org/10.1016/j.adhoc.2006.04.003, http://www.sciencedirect.com/science/article/pii/S1570870506000230
Awolusi, I., Marks, E., Hallowell, M.: Wearable technology for personalized construction safety monitoring and trending: review of applicable devices. Autom. Constr. 85, 96–106 (2018). https://doi.org/10.1016/j.autcon.2017.10.010, http://www.sciencedirect.com/science/article/pii/S0926580517309184
Behr, C.J., Kumar, A., Hancke, G.P.: A smart helmet for air quality and hazardous event detection for the mining industry. In: 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 2026–2031, March 2016. https://doi.org/10.1109/ICIT.2016.7475079
Bobek, S., Nalepa, G.J.: Uncertain context data management in dynamic mobile environments. Future Gener. Comput. Syst. 66(January), 110–124 (2017). https://doi.org/10.1016/j.future.2016.06.007
Bobek, S., Nalepa, G.J.: Uncertainty handling in rule-based mobile context-aware systems. Pervasive Mob. Comput. 39(August), 159–179 (2017). https://doi.org/10.1016/j.pmcj.2016.09.004
Bobek, S., Nalepa, G.J., Ślażyński, M.: Heartdroid - rule engine for mobile and context-aware expert systems. Expert Syst. https://doi.org/10.1111/exsy.12328. (in press)
Hass, E., Cecala, A., Hoebbel, C.L.: Using dust assessment technology to leverage mine site manager-worker communication and health behavior: a longitudinal case study. J. Progress. Res. Soc. Sci. 3, 154–167 (2016)
Hazarika, P.: Implementation of smart safety helmet for coal mine workers. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–3, July 2016. https://doi.org/10.1109/ICPEICES.2016.7853311
Kajioka, S., Mori, T., Uchiya, T., Takumi, I., Matsuo, H.: Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon. In: 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE), pp. 337–339, October 2014. https://doi.org/10.1109/GCCE.2014.7031308
Köping, L., Grzegorzek, M., Deinzer, F., Bobek, S., Ślażyński, M., Nalepa, G.J.: Improving indoor localization by user feedback. In: 2015 18th International Conference on Information Fusion (Fusion), pp. 1053–1060, July 2015
Lande, S., Matte, P.: Coal mine monitoring system for rescue and protection using zigbee. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 4(9), 3704–3710 (2015). https://doi.org/10.1016/j.proeps.2009.09.161, http://www.sciencedirect.com/science/article/pii/S1878522009001623
Mardonova, M., Choi, Y.: Review of wearable device technology and its applications to the mining industry. Energies 11(3) (2018). https://doi.org/10.3390/en11030547, http://www.mdpi.com/1996-1073/11/3/547
Mittal, A., Tiku, S., Pasricha, S.: Adapting convolutional neural networks for indoor localization with smart mobile devices. In: Proceedings of the 2018 on Great Lakes Symposium on VLSI, pp. 117–122. GLSVLSI 2018, ACM, New York (2018). https://doi.org/10.1145/3194554.3194594
Nalepa, G.J., Bobek, S.: Rule-based solution for context-aware reasoning on mobile devices. Comput. Sci. Inf. Syst. 11(1), 171–193 (2014)
Nalepa, G.J., Kutt, K., Bobek, S.: Mobile platform for affective context-aware systems. Future Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2018.02.033
Osswald, S., Weiss, A., Tscheligi, M.: Designing wearable devices for the factory: rapid contextual experience prototyping. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 517–521. May 2013. https://doi.org/10.1109/CTS.2013.6567280
Pasricha, S.: Deep underground, smartphones can save miners’ lives. Conversation UK (2016). https://theconversation.com/deep-underground-smartphones-can-save-miners-lives-64653
Ranjan, A., Misra, P., Dwivedi, B., Sahu, H.B.: Studies on propagation characteristics of radio waves for wireless networks in underground coal mines. Wirel. Pers. Commun. 97(2), 2819–2832 (2017). https://doi.org/10.1007/s11277-017-4636-y
Scheuermann, C., Heinz, F., Bruegge, B., Verclas, S.: Real-time support during a logistic process using smart gloves. In: Smart SysTech 2017, European Conference on Smart Objects, Systems and Technologies, pp. 1–8, June 2017
Thrybom, L., Neander, J., Hansen, E., Landernas, K.: Future challenges of positioning in underground mines. IFAC-PapersOnLine 48(10), 222–226 (2015). https://doi.org/10.1016/j.ifacol.2015.08.135, http://www.sciencedirect.com/science/article/pii/S2405896315010022. 2nd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics CESCIT 2015
Xu, J., Gao, H., Wu, J., Zhang, Y.: Improved safety management system of coal mine based on iris identification and RFID technique. In: 2015 IEEE International Conference on Computer and Communications (ICCC), pp. 260–264 (2015). https://doi.org/10.1109/CompComm.2015.7387578
Yi-Bing, Z.: Wireless sensor network’s application in coal mine safety monitoring. In: Zhang, Y. (ed.) Future Wireless Networks and Information Systems. LNEE, vol. 144, pp. 241–248. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27326-1_31
Zhang, K., Zhu, M., Wang, Y., Fu, E., Cartwright, W.: Underground mining intelligent response and rescue systems. Proced. Earth Planet. Sci. 1(1), 1044–1053 (2009). https://doi.org/10.1016/j.proeps.2009.09.161, http://www.sciencedirect.com/science/article/pii/S1878522009001623. Special issue title: Proceedings of the International Conference on Mining Science and Technology (ICMST 2009)
Zhang, Y., Li, L., Zhang, Y.: Research and design of location tracking system used in underground mine based on WiFi technology. In: 2009 International Forum on Computer Science-Technology and Applications, vol. 3, pp. 417–419, December 2009. https://doi.org/10.1109/IFCSTA.2009.341
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Nalepa, G.J., Brzychczy, E., Bobek, S. (2018). On the Opportunities for Using Mobile Devices for Activity Monitoring and Understanding in Mining Applications. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_9
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