{"title":"Optimal Rest Interval between Sets in Robot-Based Upper-Arm Rehabilitation","authors":"Virgil Miranda, Gissele Mosqueda, Pablo Delgado, Yimesker Yihun","volume":190,"journal":"International Journal of Biomedical and Biological Engineering","pagesStart":176,"pagesEnd":182,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10012750","abstract":"
Muscular fatigue affects the muscle activation that is needed for producing the desired clinical outcome. Integrating optimal muscle relaxation periods into a variety of health care rehabilitation protocols is important to maximize the efficiency of the therapy. In this study, four muscle relaxation periods (30, 60, 90 and 120 seconds) and their effectiveness in producing consistent muscle activation of the muscle biceps brachii between sets of an elbow flexion and extension task were investigated among a sample of 10 subjects with no disabilities. The same resting periods were then utilized in a controlled exoskeleton-based exercise for a sample size of 5 subjects and have shown similar results. On average, the muscle activity of the biceps brachii decreased by 0.3% when rested for 30 seconds, and it increased by 1.25%, 0.76% and 0.82% when using muscle relaxation periods of 60, 90 and 120 seconds, respectively. The preliminary results suggest that a muscle relaxation period of about 60 seconds is needed for optimal continuous muscle activation within rehabilitation regimens. Robot-based rehabilitation is good to produce repetitive tasks with the right intensity and knowing the optimal resting period will make the automation more effective.<\/p>","references":"[1] W. R. Frontera and J. Ochala, \u201cSkeletal muscle: a brief review of\r\nstructure and function,\u201d Calcified tissue international, vol. 96, no. 3,\r\npp. 183\u2013195, 2015.\r\n[2] R. M. Enoka and J. Duchateau, \u201cMuscle fatigue: what, why and how it\r\ninfluences muscle function,\u201d The Journal of physiology, vol. 586, no. 1,\r\npp. 11\u201323, 2008.\r\n[3] B. Bigland-Ritchie, D. Jones, G. Hosking, and R. Edwards, \u201cCentral\r\nand peripheral fatigue in sustained maximum voluntary contractions of\r\nhuman quadriceps muscle,\u201d Clinical science and molecular medicine,\r\nvol. 54, no. 6, pp. 609\u2013614, 1978.\r\n[4] Y. X. Zhi, M. Lukasik, M. H. Li, E. Dolatabadi, R. H. Wang, and\r\nB. Taati, \u201cAutomatic detection of compensation during robotic stroke\r\nrehabilitation therapy,\u201d IEEE journal of translational engineering in\r\nhealth and medicine, vol. 6, pp. 1\u20137, 2017.\r\n[5] H. W. Wallmann, \u201cChapter 5 - muscle fatigue,\u201d in Sports-Specific\r\nRehabilitation, R. Donatelli, Ed. Saint Louis: Churchill Livingstone,\r\n2007, pp. 87\u201395. [Online]. Available: https:\/\/www.sciencedirect.com\/\r\nscience\/article\/pii\/B9780443066429500083\r\n[6] B. F. De Salles, R. Simao, F. Miranda, J. da Silva Novaes, A. Lemos,\r\nand J. M. Willardson, \u201cRest interval between sets in strength training,\u201d\r\nSports medicine, vol. 39, no. 9, pp. 765\u2013777, 2009.\r\n[7] C. Wilk and B. Turkoski, \u201cProgressive muscle relaxation in cardiac\r\nrehabilitation: a pilot study,\u201d Rehabilitation Nursing, vol. 26, no. 6, pp.\r\n238\u2013242, 2001.\r\n[8] M. F. Maia, J. M. Willardson, G. A. Paz, and H. Miranda, \u201cEffects\r\nof different rest intervals between antagonist paired sets on repetition\r\nperformance and muscle activation,\u201d The Journal of Strength &\r\nConditioning Research, vol. 28, no. 9, pp. 2529\u20132535, 2014.\r\n[9] Y. Bouteraa, I. B. Abdallah, and A. Elmogy, \u201cDesign and control of an\r\nexoskeleton robot with emg-driven electrical stimulation for upper limb\r\nrehabilitation,\u201d Industrial Robot: the international journal of robotics\r\nresearch and application, 2020.\r\n[10] P. Delgado, S. Alekhya, A. Majidirad, N. A. Hakansson, J. Desai,\r\nand Y. Yihun, \u201cShoulder kinematics assessment towards exoskeleton\r\ndevelopment,\u201d Applied Sciences, vol. 10, no. 18, p. 6336, 2020.\r\n[11] W. Wang, H. Li, D. Kong, M. Xiao, and P. Zhang, \u201cA novel\r\nfatigue detection method for rehabilitation training of upper limb\r\nexoskeleton robot using multi-information fusion,\u201d International Journal\r\nof Advanced Robotic Systems, vol. 17, no. 6, p. 1729881420974295,\r\n2020.\r\n[12] A. M. Stewart, C. G. Pretty, M. Adams, and X. Chen, \u201cReview of\r\nupper limb hybrid exoskeletons,\u201d IFAC-PapersOnLine, vol. 50, no. 1,\r\npp. 15 169\u201315 178, 2017.\r\n[13] W. Wang, L. Qin, X. Yuan, X. Ming, T. Sun, and Y. Liu, \u201cBionic\r\ncontrol of exoskeleton robot based on motion intention for rehabilitation\r\ntraining,\u201d Advanced Robotics, vol. 33, no. 12, pp. 590\u2013601, 2019.\r\n[14] T. Shimano, W. J. Kraemer, B. A. Spiering, J. S. Volek, D. L. Hatfield,\r\nR. Silvestre, J. L. Vingren, M. S. Fragala, C. M. Maresh, S. J. Fleck\r\net al., \u201cRelationship between the number of repetitions and selected\r\npercentages of one repetition maximum in free weight exercises in\r\ntrained and untrained men,\u201d The Journal of Strength & Conditioning\r\nResearch, vol. 20, no. 4, pp. 819\u2013823, 2006.\r\n[15] T. Attampola Arachchige Don, \u201cDevelopment of an adaptive exoskeleton\r\nfor upper arm rehabilitaion,\u201d Ph.D. dissertation, Wichita State University,\r\n2021.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 190, 2022"}