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dc.contributor.author | Hasan, Md. Bakhtiar | |
dc.date.accessioned | 2023-04-27T08:32:05Z | |
dc.date.available | 2023-04-27T08:32:05Z | |
dc.date.issued | 2022-05-30 | |
dc.identifier.citation | 1] S. A. M. K. H. Maged S. Saeed, Asnor J. Ishak, “Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications,” Journal of Medical amp; Biology for Engineering and Computing, Elsevier, Vol. 54, p. 747–758, 2016. [Online]. Available: https://doi.org/10.1007/s11517-016-1551-4 [2] R. Dickerman, “Uthe c1-c2 vertebrae and spinal segment,” SPINE-health. [3] L. E. Long C, “Functional significance of spinal cord lesion level,” Arch Phys Med Rehabil, p. 249–255, 1955. [Online]. Available: https: //doi.org/10.1109/TSP.2017.8076013 [4] W. R, “Rehabilitation outcome following spinal cord injury,” Arch Neurol, p. 116–119, 1985. [Online]. Available: https://doi.org/10.1001/archneur.1985. 04210090068018 [5] A. R.-B. S´ebastien Mateo, “Upper limb kinematics after cervical spinal cord injury:a review,” Journal ofNeuroEngineering and Rehabilitation, p. 249–255, 2015. [Online]. Available: https://doi.org/10.1186/1743-0003-12-9 [6] E. Ibragimova, “High-fidelity prototyping: What, when, why and how?” 2016. [Online]. Available: https://blog.prototypr.io/ high-fidelity-prototyping-what-when-why-and-howf5bbde6a7fd4 [7] M. S. MH. In, B. B. Kang and K.-J. Cho., “Exo-glove: a wearable robot for the hand with a soft tendon routing system.” IEEE Robot Autom Mag 22, 1 (2015)., 2015. [Online]. Available: https://doi.org/10.1109/MRA.2014. 2362863 [8] S. S. M. H. P.Polygerinos, K. C. Galloway and C. J. Walsh., “Emg controlled soft robotic glove for assistance during activities of daily living.” 2015. [Online]. Available: https://doi.org/10.1109/ICORR.2015.7281175 34 Bibliography 35 [9] A.-M. M. G.-R. E. S´anchez-Velasco, L.E. and E. Lugo-Gonz´alez, “A low-cost emg-controlled anthropomorphic robotic hand for power and precision grasp,” iocybernetics and Biomedical Engineering, 40(1), 2020. [10] K. A.-R. G. W. C. K. A. Kaufmann T, Schulz SM, Clin Neurophysiol 2012. [Online]. Available: http://dx.doi.org/10.1016/j.clinph.2012.11.006. [11] “World health organization. 2018. who.” [Online]. Available: https: //www.who.int/news-room/fact-sheets/detail/assistive-technology. [12] M. J. G. W. Mill´an JR, Renkens F, “Noninvasive brain-actuated control of a mobile robot by human eeg,” IEEE Transactions on Biomedical Engineering, Vol 51, No. 6, 2004. [Online]. Available: https: //doi.org/10.1109/TBME.2004.827086 [13] B. J. Saharia, T. and C. Bhagabati, “Joystick controlled wheelchair.” Int. Res. J. Eng. Technol, 4(7), 2017. [14] X. W. W. R. E. S. X. Hu, K. Tong and S. Ho, “The effects of post-stroke upper-limb training with an electromyography (emg)-driven hand robot,” Journal of Electromyography and Kinesiology, vol. 23, no. 5, p. 1065–1074, 2013. [Online]. Available: https://doi.org/10.1016/j.jelekin.2013.07.007 [15] D. C. Kalantri .R.A, “The effects of post-stroke upper-limb training with an electromyography (emg)-driven hand robot,” International Journal of Engineering and Advanced Technology (IJEAT), Volume-2, 2013. [Online]. Available: https://doi.org/10.1.1.680.5066 [16] S. S. M. H. P. Polygerinos, K. C. Galloway and C. J. Walsh, “Emg controlled soft robotic glove for assistance during activities of daily living,” IEEE Intl. Conf. on Rehabilitation Robotics, 2015. [Online]. Available: https://doi.org/10.1109/ICORR.2015.7281175 [17] A. C. S. N. A. N. R. M. R. J. A. J. Ishak, S. A. Ahmad and W. Chikamune., “Design of a wireless surface emg acquisition system.” 2017. [Online]. Available: https://doi.org/10.1109/M2VIP.2017.8211481 [18] M. D. L. Lucas and Y. Matsuoka, “An emg-controlled hand exoskeleton for natural pinching,” Journal of Robot. and Mechatronics, vol. 16, p. 482–488, 2004. [Online]. Available: https://doi.org/10.1.1.476.7880 [19] G. S. Hussain, G. Spagnoletti and D. Prattichizzo, “Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in Bibliography 36 hemiparetic upper limb,” The Intl. Journal of Robotics Research, vol. 36,no. 13-14, p. 1414–1436, 2017. [Online]. Available: https://doi.org/10.1177/ 0278364917712433 [20] H. C. F. J. M. O. M. L. C. J. M. O. M. E. S. O. Thielbar, K. M.Triandafilou and D. G. Kamper, “Benefits of using a voice and emg-driven actuated glove to support occupational therapy for stroke survivors,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 3, p. 297–305, 2017. [Online]. Available: https://doi.org/10.1109/TNSRE.2016.2569070 [21] J. H. L. J. C. G. H. K. Yap, B.W. Ang and C.-H. Yeow, “A fabric-regulated soft robotic glove with user intent detection using emg and rfid for hand assistive application,” IEEE Int. Conf. on Robotics and Automation, p. 297–305, 2016. [Online]. Available: https: //doi.org/10.1109/ICRA.2016.7487535 [22] H. W. Guo, X. Sheng and X. Zhu, “Mechanomyography assisted myoeletric sensing for upperextremity prostheses: A hybrid approach,” IEEE Sensors Journal, vol. 17, p. 3100–3108, 2017. [Online]. Available: https://doi.org/10.1109/JSEN.2017.2679806 [23] Wo lczowski and R. Zdunek, “Electromyography and mechanomyography signal recognition: experimental analysis using multi-way array decomposition methods,” Biocybern Biomed Eng,vol. 37, no. 1, 2017. [Online]. Available: https://doi.org/10.1016/j.bbe.2016.09.004 [24] M. MH. In, B. B. Kang and K.-J. Cho, “Exo-glove: a wearable robot for the hand with a soft tendon routing system,” IEEE Robot Autom Mag, vol. 22, no. 1, 2015. [Online]. Available: https://doi.org/10.1109/MRA.2014.2362863 [25] H. C. K. A. S. R. V. K. X. Luo, T. Kline and D. G. Kamper, “Integration of augmented reality and assistive devices for post-stroke hand opening rehabilitation,” IEEEEMBS Intl. Conf. on Engineering in Medicine and Biology Society, 2005. [Online]. Available: https://doi.org/10.1109/ IEMBS.2005.1616080 [26] J. C. G. H. K. Yap, A. Mao and C.-H. Yeow, “Design of a wearable fmg sensing system for user intent detection during hand rehabilitation with a soft robotic glove,” IEEE Intl. Conf. on Biomedical Robotics and Biomechatronics, 2016. [Online]. Available: https://doi.org/10.1109/BIOROB.2016.7523722 Bibliography 37 [27] A. C. S. N. A. N. R. M. R. J. A.J. Ishak, S. A. Ahmad and W. Chikamune, “Design of a wireless surface emg acquisition system,” 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1–6, 2017. [Online]. Available: https://doi.org/10.1109/M2VIP.2017.8211481 [28] J. M. Nilsson, J. Ingvast and H. von Holst, “The soft extra muscle system for improving the grasping capability in neurological rehabilitation,” IEEEEMBS Conference on Biomedical Engineering and Sciences, 2012. [Online]. Available: https://doi.org/10.1109/IECBES.2012.6498090 [29] R. R. K. A. R. Zhao, J. Jalving and R. Shepherd, “A helping hand: Soft orthosis with integrated optical strain sensors and emg control,” IEEE Robot Autom Mag, vol. 23, no. 3, pp. 55 – 64, 2016. [Online]. Available: https://doi.org/10.1109/MRA.2016.2582216 [30] N. A. R. Hayder A. Azeez and M. J. A. bin safar., “”emg controlled wheelchair movement based on masseter and buccinators muscles. international journal of engineering trends and technology.37(3) (2016), 2231–5381.” 2016. [Online]. Available: https://doi.org/10.14445/22315381/IJETT-V37P221 [31] H. W. S. P. A. Sahebjad. S, Jay O’Connor and D. K. K, “Real time wheelchair control system using surface electromyographic signal analysis,” TIADIS International Conference e-Society, 2012. [32] M. N. H. M. R. K. F. A. S. M. Taslim. R, S.M. Ferdous, “A low costsurface electromyogram (semg) signal guided automated wheel chair for the disabled,” International Journal of Scientific amp; Engineering Research, Vol 3, 2012. [Online]. Available: https://doi.org/10.1.1.302.3361 [33] S. L. Giho Jang, Junghoon Kim and Y. Choi., “Emg- based continuous control scheme with simple classifier for electric- powered wheelchair.” IEEE Transactions on Industrial Electronics 63, 6 (2016), 3695–3705., 2016. [Online]. Available: https://doi.org/10.1109/TIE.2016.252238 [34] Mahendran and Rampriya., “Emg signal based control of an intelligent wheelchair.” 2014. [Online]. Available: https://doi.org/10.1109/ICCSP.2014. 6950055 [35] R. B. A.B. Jani and A. K. Roy., “Design of a low-power, low-cost ecg emg sensor for wearable biometric and medical application.” IEEE SENSORS (2017), 1–3., 2017. [Online]. Available: https://doi.org/10.1109/ICSENS. 2017.8234427 Bibliography 38 [36] S. V. Baspinar U, Barol HS, “Performance comparison of artificial neural network and gaussian mixture model in classifying hand motions by using semg signals.” Biocybern Biomed Eng 2013;33(1):33–5. [Online]. Available: http://dx.doi.org/10.1016/S0208-5216(13)70054-8 [37] A. Y. A. M. Barabulut D, Ortes F, “Comparative evaluation of emg signals features for myoelectric controlled human arm prosthetics.” Biocybern Biomed Eng 2017;37(2):326–35. [Online]. Available: http://dx.doi.org/10. 1016/j.bbe.2017.03.001 [38] V. A. Hakonen M, Piitulainen H, “Current state of digital signal processing in myoelectric interfaces and related applications.” Biomed Signal Process Control 2015;18:334–59. [Online]. Available: http://dx.doi.org/10.1016/j. bspc.2015.02.009 [39] R. R. K. M. L. J. G. W. R. M. Russo RE, Fernandez JG, “Algorithm of myoelectric signals processing for the control of prosthetic robotic hands.” J Com Sci Tech 2018;18(1):28–34. [Online]. Available: http://dx.doi.org/10.24215/16666038.18.e04 [40] Z. X. Wang N, Lao K, “Design and myoelectric control of an anthropomorphic prosthetic hand.” J Bionic Eng 2017;14 (1):47–59. [Online]. Available: http://dx.doi.org/10.1016/S1672-6529(16)60377-3 [41] S. V. C. V. Sathish. S, K. Nithyakalyani and J. Sivaraman, “Control of robotic wheel chair using emg signals for paralysed person,” Indian Journal of Science and Technology, Vol 9(1), 2016. [Online]. Available: https://doi.org/10.17485/ijst/2016/v9i37/102547 [42] W. X. C. W. H. L. J. Z. J. Qi, S. and J. Wang, “semg-based recognition of composite motion with convolutional neural network,” Sensors and Actuators A: Physical, Vol 311, 2020. [Online]. Available: https://doi.org/10.1016/j.sna.2020.112046 [43] M. E. Benalc´azar, C. Motoche, J. A. Zea, A. G. Jaramillo, C. E. Anchundia, P. Zambrano, M. Segura, F. Benalc´azar Palacios, and M. P´erez, “Real-time hand gesture recognition using the myo armband and muscle activity detection,” IEEE Second Ecuador Technical Chapters Meeting (ETCM), 2017. [Online]. Available: https://doi.org/10.1109/ETCM. 2017.8247458 Bibliography 39 [44] B. R. N. Z. G. I. Ma. Y., P. Chen and Donati.E., “Emg-based gestures classification using a mixed-signal neuromorphic processing system.” IEEE J. Emerg. Top. Circuits Syst. 10 (2020)., 2020. [Online]. Available: https://doi.org/10.1109/JETCAS.2020.3037951 [45] K. T. A. Huang, H. and R. D. Lipschutz, “A strategy for identifying locomotion modes using surface electromyography,” IEEE Trans. Biomed. Eng. vol 56, 2009. [Online]. Available: https://doi.org/10.1109/TBME.2008. 2003293 [46] K. Englehart and B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng. vol 40, 2003. [Online]. Available: https://doi.org/10.1109/TBME.2003.813539 [47] H. S. J. N. E. K. B. F. D. Nielsen, J. L. G. and P. A. Parker, “Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training,” IEEE Trans. Biomed. Eng. vol 58, 2011. [Online]. Available: https://doi.org/10.1109/TBME.2010.2068298 [48] M. C. M. C. Kim KS, Choi HH, “Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions,” Current applied physics, vol 11, 2011. [Online]. Available: https://doi.org/10.1016/j. cap.2010.11.051 [49] Z. W. A. M. Doulah ABMSU, Fattah SA, “Wavelet domain feature extraction scheme based on dominant motor unit action potential of emg signal for neuromuscular disease classification,” IEEE Transaction Biomedical Circuits and Systems, vol 8, 2014. [Online]. Available: https: //doi.org/10.1109/TBCAS.2014.2309252 [50] H.-W. A. Yousefi J, “Characterizing emg data using machine- learning tools.” Computers in biology and medicine. 2014; 51: 1-13. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2014.04.018 [51] E. A. Khazaee A, “Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral,” features. Biomedical Signal Processing and Control., 2010;. [Online]. Available: https: //doi.org/10.1016/j.bspc.2010.07.006 [52] R. S. Venugopal G, Navaneethakrishna M, “Extraction and analysis of multiple time window features associated with muscle fatigue conditions using Bibliography 40 semg signals.” Expert Systems with Applications. 2014; 41(6): 2652-2659. [Online]. Available: https://doi.org/10.1016/j.eswa.2013.11.009 [53] L. Breiman, “Random forests. mach. learn.” 2001. [54] M. Liaw, A.; Wiener, “Classification and regression by randomforest.” R News 2002, 2, 18–22. [55] Y. G. Y. Z. Y. Z. J. Xiao, F.; Wang, “Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests.” Biomed. Signal Process. 2018, 39, 303–311. [Online]. Available: https://doi.org/10.1016/j.bspc.2017.08.015 [56] A. Breiman, L.; Cutler, “Random forests.” [Online]. Available: https://www.stat.berkeley.edu/breiman/RandomForests/cchome. htm(ac-cessedon21September2019). [57] T. M. Cover and P. E. Hart, ““nearest neighbor pattern classification,”,” IEEE Trans. Inform. Theory, vol. I T-13, pp. 21–26,1967. [58] H. Fayed and A. Atiya, ““a novel template reduction approach for the knearest neighbor method”,” IEEE Trans on Neural Network, Vol. 20, No. 5, May 2009.pp:890-896. | en_US |
dc.identifier.uri | http://hdl.handle.net/123456789/1849 | |
dc.description | Supervised by Dr. Md. Kamrul Hasan, Professor, Department of Computer Science and Engineering, Islamic University of Technology.(IUT), Board Bazar, Gazipur-1704, Bangladesh. This thesis submitted in partial fulfilment of the requirements for the degree of M.Sc. in Computer Science and Engineering | en_US |
dc.description.abstract | The most significant work for a service supervisor is to discover hidden flaws in car engines in order to control the engines to be safe and increase the reliability of vehicle systems. We will provide a STUDY THE METHODOLOGY OF FAULT DIAGNOSIS IN Gasoline Engines BY USING Computer assisted ENGINE ANALYZER and the extension theory in this thesis, and we will also apply this approach to the fault identification of a real vehicle engine. The proposed detection methodology was put to the test on the engine with computer analyzers, and it was compared to other conventional different techniques. The proposed method may also be used to detect hidden faults in automobile engines, as demonstrated by the experimental findings. The CMT range of Diagnostic System offers comprehensive engine system checks through a modern PC-BASED platform. This allows each machine to be easily upgraded to incorporate new test routine, data, and application modules as DIESEL TEST, etc. The printing of test results and is easy through the inbuilt printer and on some models the test results can be saved to disc. The CMT comes with several pre-installed software packages. These have been designed to make the technician's job more straightforward. This should improve the first-time fix rate which in turn will improve customer retention and increase profits. Help is available on screen for vehicle connections, machine operation and test procedures. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh | en_US |
dc.subject | Diagnosis of faults in petrol engines | en_US |
dc.title | Study the methodology of diagnosis of faults in petrol engines by using computerised engine analyser | en_US |
dc.type | Thesis | en_US |