Speed controller-based fuzzy logic for a biosignal-feedbacked cycloergometer

References

• Al-Kasasbeh R, Korenevskiy N, Ionescou F, Alshamasin M, Kuzmin A. 2012. Prediction and prenosological diagnostics of heart diseases based on energy characteristics of acupuncture points and fuzzy logic. Comput Methods Biomech Biomed Eng. 15(7):681–689.
   PubMed Web of Science ®Google Scholar
• Apostolopoulos ID, Groumpos PP. 2020. Non-invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps. Comput Methods Biomech Biomed Eng. 23(12):879–887.
   PubMed Web of Science ®Google Scholar
• Argha A, Su SW, Lee S, Nguyen H, Celler BG. 2014. On heart rate regulation in cycle-ergometer exercise. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; August 26–30, 2014; Illinois: IEEE.
   Google Scholar
• Argha A, Ye L, Su SW, Nguyen H, Celler BG. 2016. Heart rate regulation during cycle-ergometer exercise using damped parameter estimation method. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); August 16–20; Orlando, FL: IEEE.
   Google Scholar
• del R Millan J, Renkens F, Mourino J, Gerstner W. 2004. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng. 51(6):1026–1033.
   PubMed Web of Science ®Google Scholar
• e Zehra Baig D, Ahmad R, Savkin A, Celler B. 2019. Robust adaptive H ∞ control design for heart rate regulation during rhythmic exercises of unknown type. Int J Adapt Control Signal Process. 33(5):843–854.
   Web of Science ®Google Scholar
• e Zehra Baig D, Javed F, Savkin AV, Celler BG. 2011. An adaptive H ∞ control design for exercise-independent human heart rate regulation system. In: 2011 9th IEEE International Conference on Control and Automation (ICCA); December; IEEE.
   Google Scholar
• García-Martínez JR, Cruz-Miguel EE, Carrillo-Serrano RV, Mendoza-Mondragón F, Toledano-Ayala M, Rodríguez-Reséndiz J. 2020. A PID-type fuzzy logic controller-based approach for motion control applications. Sensors. 20(18):5323.
   Web of Science ®Google Scholar
• Iancu I. 2018. Heart disease diagnosis based on mediative fuzzy logic. Artif Intell Med. 89:51–60.
   PubMed Web of Science ®Google Scholar
• Lin Q, Li T, Shakeel PM, Samuel RDJ. 2021. Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management. J Ambient Intell Human Comput. 12(3):3329–3340.
   Web of Science ®Google Scholar
• Miramontes I, Guzman J, Melin P, Prado-Arechiga G. 2018. Optimal design of interval type-2 fuzzy heart rate level classification systems using the bird swarm algorithm. Algorithms. 11(12):206.
   Web of Science ®Google Scholar
• Oldridge N, Taylor RS. 2020. Cost-effectiveness of exercise therapy in patients with coronary heart disease, chronic heart failure and associated risk factors: a systematic review of economic evaluations of randomized clinical trials. Eur J Prev Cardiol. 27(10):1045–1055.
   PubMed Web of Science ®Google Scholar
• Ortiz-Echeverri CJ, Salazar-Colores S, Rodríguez-Reséndiz J, Gómez-Loenzo RA. 2019. A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network. Sensors. 19(20):4541.
   Web of Science ®Google Scholar
• Pancardo P, Hernández-Nolasco JA, Acosta-Escalante F. 2018. A fuzzy logic-based personalized method to classify perceived exertion in workplaces using a wearable heart rate sensor. Mobile Inf Syst. 2018:1–17.
   Web of Science ®Google Scholar
• Paradiso M, Pietrosanti S, Scalzi S, Tomei P, Verrelli CM. 2013. Experimental heart rate regulation in cycle-ergometer exercises. IEEE Trans Biomed Eng. 60(1):135–139.
   PubMed Web of Science ®Google Scholar
• Park SY, Yun DY, Kim T, Lee JY, Lee D. 2020. An energy efficient enhanced dual-fuzzy logic routing protocol for monitoring activities of the elderly using body sensor networks. Electronics. 9(5):723.
   Web of Science ®Google Scholar
• Peláez-Aguilera MD, Espinilla M, Olmo MRF, Medina J. 2019. Fuzzy linguistic protoforms to summarize heart rate streams of patients with ischemic heart disease. Complexity. 2019:1–11.
   Google Scholar
• Reddy GT, Reddy MPK, Lakshmanna K, Rajput DS, Kaluri R, Srivastava G. 2020. Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evol Intel. 13(2):185–196.
   Web of Science ®Google Scholar
• Rodríguez-Guerrero C, Fraile JC, Pérez-Turiel J, Farina PR. 2011. Robot biocooperativo con modulación háptica para tareas de neurorehabilitación de los miembros superiores. Rev Iberoamericana Autom Inform Ind RIAI. 8(2):63–70.
   Google Scholar
• Romero AL, Rodriguez RM, Martinez L. 2020. Computing with comparative linguistic expressions and symbolic translation for decision making: ELICIT information. IEEE Trans Fuzzy Syst. 28(10):2510–2522.
   Web of Science ®Google Scholar
• Sallam H, Hashmi A. 2019. Fuzzy logic: theory and healthcare application. Am J Manag. 19(3):122–131.
   Google Scholar
• Santiago L. 2018. On motion. https://www.onmotion.online/. [Last accessed March 5, 2021]
   Google Scholar
• Santos J, Torres-Machi C, Morillas S, Cerezo V. 2020. A fuzzy logic expert system for selecting optimal and sustainable life cycle maintenance and rehabilitation strategies for road pavements. Int J Pavement Eng. 1–13.
   Web of Science ®Google Scholar
• Saulnier P, Sharlin E, Greenberg S. 2009. Using bio-electrical signals to influence the social behaviours of domesticated robots. In: Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction - HRI ’09; California: ACM Press.
   Google Scholar
• Saxena K, Banodha U. 2019. A fuzzy logic based cardiovascular disease risk level prediction system in correlation to diabetes and smoking. In: Data management, analytics and innovation. Singapore: Springer; p. 29–40.
   Google Scholar
• Scalzi S, Tomei P, Verrelli CM. 2012. Nonlinear control techniques for the heart rate regulation in treadmill exercises. IEEE Trans Biomed Eng. 59(3):599–603.
   PubMed Web of Science ®Google Scholar
• Singh P, Kumar V, Rana KPS. 2020. Speed control of a nonlinear DC motor using fuzzy PD + I controller. In: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON); October 2–4, 2020; India: IEEE.
   Google Scholar
• Su SW, Wang L, Celler Bg Savkin Av, Guo Y. 2006. Modelling and control for heart rate regulation during treadmill exercise. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society; 30 aug–3 sep. New York, NY: IEEE.
   Google Scholar
• Toledo-Pérez D, Martínez-Prado M, Gómez-Loenzo R, Paredes-García W, Rodríguez-Reséndiz J. 2019. A study of movement classification of the lower limb based on up to 4-EMG channels. Electronics. 8(3):259.
   Web of Science ®Google Scholar
• Tucan P, Gherman B, Major K, Vaida C, Major Z, Plitea N, Carbone G, Pisla D. 2020. Fuzzy logic-based risk assessment of a parallel robot for elbow and wrist rehabilitation. Int J Environ Res Public Health. 17(2):654.
   Web of Science ®Google Scholar
• Wang CH, Hor KC. 2019. From fuzzy center average defuzzifier (CAD) to fuzzy lookup table controller (FLTC) with an efficient heaviside search algorithm (HSA). Neural Comput Appl. 31(9):5135–5145.
   Web of Science ®Google Scholar
• Wang H, Bai W, Zhao X, Liu PX. 2021. Finite-time-prescribed performance-based adaptive fuzzy control for strict-feedback nonlinear systems with dynamic uncertainty and actuator faults. IEEE Trans Cybern. 1–13.
   Google Scholar
• Wang L, Wang H, Liu PX, Ling S, Liu S. 2020. Fuzzy finite-time command filtering output feedback control of nonlinear systems. IEEE Trans Fuzzy Syst. 1.
   Google Scholar
• Wu B, Yip TL, Yan X, Soares CG. 2019. Fuzzy logic based approach for ship-bridge collision alert system. Ocean Eng. 187:106152.
   Web of Science ®Google Scholar