Abstract
The aim of this article is to propose a conceptual framework for a system, using which a severely ill or disabled patient admitted in a hospital cabin can exert some control on the surrounding environment using only his/her thoughts. A brain computer interface (BCI) headset can be used to collect Electroencephalographic (EEG) data from the brain and an artificial neural network can be used to process the EEG data and predict what the patient is thinking. The proposed system will provide immense benefits to the patients admitted in hospital cabins, especially in developing countries, where the doctor to patient and nurse to patient ratio is really low and a patient admitted in a hospital cabin is usually left unattended for a long period of time.
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Abiri, R., Borhani, S., Kilmarx, J., Esterwood, C., Jiang, Y., Zhao, X.: A usability study of low-cost wireless brain-computer interface for cursor control using online linear model. IEEE Trans. Hum.-Mach. Syst. 50(4), 287–297 (2020)
Akter, N., Akter, M., Turale, S.: Barriers to quality of work life among Bangladeshi nurses: a qualitative study. Int. Nurs. Rev. 66(3), 396–403 (2019)
Banik, B.C., Ghosh, M., Das, A., Banerjee, D., Paul, S., Neogi, B.: Design of mind-controlled vehicle (mcv) & study of EEG signal for three mental states. In: 2017 Devices for Integrated Circuit (DevIC), pp. 808–812. IEEE (2017)
Blankertz, B., Krauledat, M., Dornhege, G., Williamson, J., Murray-Smith, R., Müller, K.R.: A note on brain actuated spelling with the berlin brain-computer interface. In: International Conference on Universal Access in Human-Computer Interaction, pp. 759–768. Springer, Heidelberg (2007)
Darkwa, E.K., Newman, M.S., Kawkab, M., Chowdhury, M.E.: A qualitative study of factors influencing retention of doctors and nurses at rural healthcare facilities in Bangladesh. BMC Health Serv. Res. 15(1), 1–12 (2015)
Dipta, S.S., Ghosh, A., Kundu, A., Saha, A.: 2-d motion based real time wireless interaction system for disabled patients. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 331–334. IEEE (2019)
Diva, S.Z., Prorna, R.A., Rahman, I.I., Islam, A.B., Islam, M.N.: Applying brain-computer interface technology for evaluation of user experience in playing games. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. IEEE (2019)
Dornhege, G., Millán, J.D.R., Hinterberger, T., McFarland, D., Müller, K.R., et al.: Toward Brain-Computer Interfacing, vol. 63. MIT press, Cambridge (2007)
Goel, M.K.: An overview of brain computer interface. In: 2015 Recent and Emerging trends in Computer and Computational Sciences (RETCOMP), pp. 10–17. IEEE (2015)
Inan, T.T., Samia, M.B.R., Tulin, I.T., Islam, M.N.: A decision support model to predict icu readmission through data mining approach. In: 22nd Pacific Asia Conference on Information Systems (PACIS 2018) (2018)
Islam, M.N., Islam, A.N.: A systematic review of the digital interventions for fighting covid-19: the Bangladesh perspective. IEEE Access 8, 114078–114087 (2020)
Islam, M.N., Islam, I., Munim, K.M., Islam, A.N.: A review on the mobile applications developed for covid-19: an exploratory analysis. IEEE Access 8, 145601–145610 (2020)
Islam, M.N., Karim, M.M., Inan, T.T., Islam, A.N.: Investigating usability of mobile health applications in Bangladesh. BMC Med. Inf. Decis. Making 20, 19 (2020)
Jang, W.A., Lee, S.M., Lee, D.H.: Development bci for individuals with severely disability using emotiv eeg headset and robot. In: 2014 International Winter Workshop on Brain-Computer Interface (BCI). pp. 1–3. IEEE (2014)
Lamb, K., Madhe, S.: Hand gesture recognition based bed position control for disabled patients. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 170–174. IEEE (2016)
Lee, W.T., Nisar, H., Malik, A.S., Yeap, K.H.: A brain computer interface for smart home control. In: 2013 IEEE International Symposium on Consumer Electronics (ISCE), pp. 35–36. IEEE (2013)
Liao, C.Y., Chen, R.C., Tai, S.K.: Emotion stress detection using EEG signal and deep learning technologies. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 90–93. IEEE (2018)
Lo, C.C., Chien, T.Y., Pan, J.S., Lin, B.S.: Novel non-contact control system for medical healthcare of disabled patients. IEEE Access 4, 5687–5694 (2016)
Mahmud, S.: Health workforce in Bangladesh (2013). https://opinion.bdnews24.com/2013/03/24/health-workforce-in-bangladesh/
McGarry, B.E., Grabowski, D.C., Barnett, M.L.: Severe staffing and personal protective equipment shortages faced by nursing homes during the covid-19 pandemic: study examines staffing and personal protective equipment shortages faced by nursing homes during the covid-19 pandemic. Health Aff. 39(10), 1812–1821 (2020)
Panoulas, K.J., Hadjileontiadis, L.J., Panas, S.M.: Brain-computer interface (bci): types, processing perspectives and applications. In: Multimedia Services in Intelligent Environments, pp. 299–321. Springer, Heidelberg (2010)
Prashant, P., Joshi, A., Gandhi, V.: Brain computer interface: a review. In: 2015 5th Nirma University International Conference on Engineering (NUiCONE), pp. 1–6. IEEE (2015)
Ratchatorn, T., Pumrin, S.: Patient aid message notification system based on hand movement tracking and haar-like features. In: 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 624–627. IEEE (2018)
Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M.H., Burdet, E.: A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 18(6), 590–598 (2010)
Reinhold, K., Tint, P., Traumann, A., Tamme, P., Tuulik, V., Voolma, S.R.: Digital support in logistics of home-care nurses for disabled and elderly people. In: International Conference on Human Interaction and Emerging Technologies, pp. 563–568. Springer, Heidelberg (2019)
Shantala, C., Rashmi, C.: Mind controlled wireless robotic arm using brain computer interface. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–8. IEEE (2017)
Shih, J.J., Krusienski, D.J., Wolpaw, J.R.: Brain-computer interfaces in medicine. In: Mayo Clinic Proceedings, vol. 87, pp. 268–279. Elsevier (2012)
Soman, S., Murthy, B.: Using brain computer interface for synthesized speech communication for the physically disabled. Procedia Comput. Sci. 46, 292–298 (2015)
St, L., Wold, S., et al.: Analysis of variance (anova). Chemometr. Intell. Lab. Syst. 6(4), 259–272 (1989)
Sterpetti, A.V.: Lessons learned during the covid-19 virus pandemic. J. Am. Coll. Surg. 230(6), 1092–1093 (2020)
Sudarsanan, K., Sasipriya, S.: Controlling a robot using brain waves. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–4. IEEE (2014)
Sultana, M., Hossain, A., Laila, F., Taher, K.A., Islam, M.N.: Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology. BMC Med. Inf. Decis. Mak. 20(1), 1–10 (2020)
Swaminathan, R., Prasad, S.: Brain computer interface used in health care technologies. In: Next Generation DNA Led Technologies, pp. 49–58. Springer, Heidelberg (2016)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol 113(6), 767–791 (2002)
Gao, X., Xu, D., Cheng, M., Gao, S.: A BCI-based environmental controller for the motion-disabled. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 137–140 (2003)
Yeom, H.G.: Trends and future of brain-computer interfaces. In: 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), pp. 785–788. IEEE (2018)
Yordanov, Y., Tsenov, G., Mladenov, V.: Humanoid robot control with eeg brainwaves. In: 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol. 1, pp. 238–242. IEEE (2017)
Zhu, W., Zeng, N., Wang, N., et al.: Sensitivity, specificity, accuracy, associated confidence interval and roc analysis with practical SAS implementations. In: NESUG Proceedings: Health Care and Life Sciences, Baltimore, Maryland, vol. 19, p. 67 (2010)
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Aadeeb, M.S., Munna, M.M.H., Rahman, M.R., Islam, M.N. (2021). Towards Developing a Hospital Cabin Management System Using Brain Computer Interaction. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_20
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