Abstract
Human adaptive mechatronics (HAM) device is operated using neuromuscular interface controller. The muscle activity is monitored with the help of electromyogram (EMG) signals. The change in signal strength due to the volunteering movement of body parts is detected by placing few electrodes on the skin surface. EMG signal is used as the control signal and it gives the movement coordination. The amplitude of the signal changes with respect to the muscle activity. The HAM device is made to operate depending upon the amplitude changes in the generated EMG signal. The option of using cybernetic loop facilitates the balance of the control error and the delayed response time of the system. This paper elaborates the working of human adaptive neuromuscular interface controller in mechatronics device and the simulation is carried out in real time approach for the different muscle activity mainly between the two actions: volunteer motion and intended motion.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
14 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04136-w
References
Abdulkader SN, Ayman Atia A et al (2015) Brain computer interfacing: applications and challenges. Egypt Inform J 16(2):213–230. https://doi.org/10.1016/j.eij.2015.06.002
Abedi M, Moghaddam MM, Mohammad S, Firoozabadi P (2015) A neuromechanical modeling of spinal cord injury locomotor system for simulating the rehabilitation effects. Biocybern Biomed Eng 36(1):193–204. https://doi.org/10.1016/j.bbe.2015.12.002
Alibeji NA, Molazadeh V, Moore-Clingenpeel F, Sharma N (2018) A muscle synergy-inspired control design to coordinate functional electrical stimulation and a powered exoskeleton: artificial generation of synergies to reduce input dimensionality. IEEE Control Syst Mag 38(6):35–60. https://doi.org/10.1109/MCS.2018.2866603
Bae S, Han SM, Moura S (2019) Modeling & control of human actuated systems. IFAC. https://doi.org/10.1016/.ifacol.2019.0.1.016
Bellman MJ, Downey RJ, Parikh A, Dixon WE (2017) Automatic control of cycling induced by functional electrical stimulation with electric motor assistance. IEEE Trans Autom Sci Eng 14(2):1225–1234. https://doi.org/10.1109/TASE.2016.2527716
Botelho T et al (2017) Pseudo-online multimodal interface based on movement prediction for lower limbs rehabilitation. In: Ibáñez J, González-Vargas J, Azorín J, Akay M, Pons J (eds) Converging clinical and engineering research on neurorehabilitation II. Biosystems & biorobotics, vol 15. Springer, Cham
Calatayud J, Pérez-Alenda S, Carrasco JJ et al (2020) Electromyographic and safety comparisons of common lower limb rehabilitation exercises for people with haemophilia. Phys Ther 100(1):116–126. https://doi.org/10.1093/ptj/pzz146
Campeau-Lecours A, Otis M, Belzile P-L et al (2016) A time-domain vibration observer and controller for physical human–robot interaction. Mechatronics 36:45–53. https://doi.org/10.1016/j.mechatronics.2016.04.006
Chew E, Turner D (2019) Can a robot bring your life back? A systematic review for robotics in rehabilitation. Adv Exp Med Biol 1170:1–35. https://doi.org/10.1007/978-3-030-24230-5_1
Chowdhury A, Raza H, Meena Y, Dutta A, Prasad G (2018) An EEG-EMG correlation-based brain–computer interface for hand orthosis supported neuro-rehabilitation. J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2018.11.010
Crespo LM, Riener R (2018) Robot assisted gait training. Rehabil Robot 16:227–240. https://doi.org/10.1016/B978-0-12-811995-2.00016-3
Czerniak JN et al (2019) Systematic approach to develop a flexible adaptive human–machine interface in socio-technological systems. In: Bagnara S, Tartaglia R, Albolino S, Alexander T, Fujita Y (eds) Proceedings of the 20th congress of the international ergonomics association (IEA 2018). Advances in intelligent systems and computing, vol 825. Springer, Cham
Darwish A, Hassanien AE (2018) Cyber physical systems design, methodology, and integration: the current status and future outlook. J Ambient Intell Humaniz Comput 9:1541–1556. https://doi.org/10.1007/s12652-017-0575-4
Diaz-Hernandez O, Garcia-Villalpando S (2017) Mechatronic system for rehabilitation of patients after a stroke, 2017 IEEE XXIV international conference on electronics, electrical engineering and computing (INTERCON), Cusco, 2017, pp 1–4. https://doi.org/10.1109/INTERCON.2017.8079654
Dixon W (2018) Control systems for assistive and rehabilitation robotics: a introduction to the special issue. In: IEEE control systems magazine, vol 38, no 6, pp 32–34. https://doi.org/10.1119/MCS.208.2866602
Fairclough SH (2017) Physiological computing and intelligent adaptation. In Jeon M (ed) Emotions and affect in human factors and human–computer interaction. Elsevier Academic Press, Amsterdam, pp 539–556. https://doi.org/10.1016/B978-0-12-801851-4.00020-3.
Gassert R, Dietz V (2018) Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective. J NeuroEng Rehabil 15:46. https://doi.org/10.1186/s12984-018-0383-x
Hashimoto Y, Kakui T, Ushiba J, Liu M et al (2018) Development of rehabilitation system with brain-computer interface for subacute stroke patients. In: 2018 IEEE international conference on systems, man, and cybernetics (SMC), Miyazaki, Japan, pp 51–56. https://doi.org/10.1109/SMC.2018.00018
Jamal MZ, Lee D, Hyun DJ (2019) Real time adaptive filter based EMG signal processing and instrumentation scheme for robust signal acquisition using dry EMG electrodes. In: 2019 16th international conference on ubiquitous robots (UR), Jeju, Korea (South), 2019, pp 683–688
Jamwal PK, Hussain S, Ghayesh MH et al (2017) Adaptive impedance control of parallel ankle rehabilitation robot. ASME J Dyn Syst Meas Control 139(11):11006. https://doi.org/10.1115/1.4036560
Khoshdel V, Akbarzadeh A, Naghavi N et al (2018) sEMG-based impedance control for lower-limb rehabilitation robot. Intell Serv Robot 11:97–108. https://doi.org/10.1007/s11370-017-0239-4
Kim J, Chung K (2020) Neural-network based adaptive context prediction model for ambient intelligence. J Ambient Intell Humaniz Comput 11:1451–1458. https://doi.org/10.1007/s12652-018-0972-3
Labonte-Lemoyne E, Courtemanche F, Louis V et al (2018) Dynamic threshold selection for a biocybernetic loop in an adaptive video game context. Front Hum Neurosci 12:282. https://doi.org/10.3389/fnhum.2018.00282
Li J, Wang Y, Zhang L, Jung T (2012) Combining ERPs and EEG spectral features for decoding intended movement direction. In: Annual international conference of the IEEE engineering in medicine and biology society, San Diego, CA, 2012, pp 1769–1772. https://doi.org/10.1109/EMBC.2012.6346292
Ma J, Chen S-L, Teo CS et al (2019) Parameter space optimization towards integrated mechatronics design for uncertain systems with generalized feedback constraints 2019. Elsevier Automatica 105:149–158. https://doi.org/10.1016/j.automatica.2019.03.010
McCall JV (2019) Performance of a powered ankle exoskeleton using neuromuscular model-based control over a range of walking speeds. Theses North carolina State University. http://www.lib.ncsu.edu/resolver/1840.20/36937
Muñoz J, Gouveia E, Cameirão M, Bermúdez i Badia S (2017) The biocybernetic loop engine: an integrated tool for creating physiologically adaptive videogames, physiological computing systems (PhyCS-2017), At Madrid, España, vol 2017. https://doi.org/10.5220/0006429800450054
Rahman MA, Hossain MS, Rashid MM et al (2019) A blockchain-based non-invasive cyber-physical occupational therapy framework: BCI perspective. IEEE Access 7:34874–34884. https://doi.org/10.1109/ACCESS.2019.2903024
Rea P, Ottaviano E (2020) Mechatronic design of low-cost control systems for rehabilitation and assisting devices. In: Habib M (ed) Handbook of research on advanced mechatronic systems and intelligent robotics. IGI Global, Hershey, pp 82–97. https://doi.org/10.4018/978-1-7998-0137-5.ch004.
Rouse CA, Cousin CA, Duenas VH et al (2019) FES and motor assisted cycling to track power and cadence to desired voluntary bounds. IFAC. https://doi.org/10.1016/j.ifacol.2019.01.014
Sarcevic P, Kincses Z, Pletl S (2019) Online human movement classification using wrist-worn wireless sensors. J Ambient Intell Humaniz Comput 10:89–106. https://doi.org/10.1007/s12652-017-0606-1
Sawada T, Kaneko F, Aoyama T et al (2017) Analysis of reaching movements in stroke patients using average variability of electromyogram value. Asian J Occup Ther 13:13–21. https://doi.org/10.11596/asiajot.13.13
Seth A, Hicks JL, Uchida TK et al (2018) OpenSim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 14(7):e1006223. https://doi.org/10.1371/journal.pcbi.1006223
Simonetti D, Tagliamonte NL, Zoro L et al (2018) Biomechatronic design criteria of systems for robot-mediated rehabilitation therapy. Rehabil Robot. https://doi.org/10.1016/B978-0-12-811995-2.00032-1
Song MS, Kang S-G et al (2019) Wireless skin mountable EMG sensor for human machine interface application MDPI. Micromechanics 10:879. https://doi.org/10.3390/mi10120879
Tang J, Zhou Z, Yu Y (2016) A hybrid computer interface for robot arm control. In: 8th international conference on information technology in medicine and education (ITME), Fuzhou, 2016, pp 365–369. https://doi.org/10.1109/ITME.2016.0088
Tariq M, Trivailo PM, Simic M (2018) EEG based brain computer interface control schemes for lower limb assistive robots. Front Hum Neurosci 12:312. https://doi.org/10.3389/fnhum.2018.00312
United Nations, World Population Ageing 2019: Highlights (ST/ESA/SER.A/430), Department of Economic and Social Affairs, Population Division (2019)
Wu Q, Wang X, Chen B et al (2018) Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton. Mechatronics 53:85–94. https://doi.org/10.1016/j.mechatronics.2018.05.014
Yeatman M, Lv G, Gregg RD (2019) Decentralized passivity-based control with a generalized energy storage function for robust biped locomotion. ASME J Dyn Syst Meas Control 141(10):101007. https://doi.org/10.1115/1.4043801
Yue Z, Zhang X, Wang J (2017) Hand rehabilitation robotics on post stroke motor recovery. Behav Neurol. https://doi.org/10.1155/2017/3908135
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04136-w
About this article
Cite this article
Joel, J.P.A., Raj, R.J.S., Durai, C.A.D. et al. RETRACTED ARTICLE: Human adaptive mechatronics system integrated with cybernetics loop using neuromuscular controller in occupational therapy for elderly person with disability. J Ambient Intell Human Comput 12, 7299–7308 (2021). https://doi.org/10.1007/s12652-020-02405-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02405-0