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Journal of NeuroEngineering and Rehabilitation

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Open Access 01-12-2018 | Research

Muscle fatigue assessment during robot-mediated movements

Authors: Maddalena Mugnosso, Francesca Marini, Michael Holmes, Pietro Morasso, Jacopo Zenzeri

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2018

Abstract

Background

Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.

Methods

40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects’ effort throughout the task, the energy consumption was estimated.

Results

From the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.

Conclusion

The robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.

Silverthorn DU. Human Physiology : an Integrated Approach. 1st Ed. Upper Saddle River: Pearson Prentice Hall; 1998.

Ma L, Chablat D, Bennis F, Zhang W, Hu B, Guillaume F. A novel approach for determining fatigue resistances of different muscle groups in static cases. Int J Ind Ergon. 2011; 41(1):10–8. https://doi.org/10.1016/j.ergon.2010.11.005.CrossRef

Monjo F, Terrier R, Forestier N. Muscle fatigue as an investigative tool in motor control: A review with new insights on internal models and posture–movement coordination. Hum Mov Sci. 2015; 44:225–33. https://doi.org/10.1016/j.humov.2015.09.006.PubMedCrossRef

Vafadar AK, Côté JN, Archambault PS. The Effect of Muscle Fatigue on Position Sense in an Upper Limb Multi-joint Task. Mot Control. 2012; 16(2):265–83. https://doi.org/10.1123/mcj.16.2.265.CrossRef

Zwarts MJ, Bleijenberg G, van Engelen BGM. Clinical neurophysiology of fatigue. Clin Neurophysiol. 2008; 119(1):2–10. https://doi.org/10.1016/j.clinph.2007.09.126.PubMedCrossRef

Thacham-Poyil A, Amirabdollahian F, Steuber V. Study of Gross Muscle Fatigue During Human-Robot Interactions. IARIA. 2017. http://vuh-la-uhra.herts.ac.uk/handle/2299/18352http://uhra.herts.ac.uk/handle/2299/18352.

Angelini C, Tasca E. Fatigue in muscular dystrophies. Neuromuscul Disord. 2012; 22:214–20. https://doi.org/10.1016/j.nmd.2012.10.010.CrossRef

Shimizu-Motohashi Y, Miyatake S, Komaki H, Takeda S, Aoki Y. Recent advances in innovative therapeutic approaches for Duchenne muscular dystrophy: from discovery to clinical trials,. Am J Transl Res. 2016; 8(6):2471–89.PubMedPubMedCentral

McDonald CM, Henricson EK, Han JJ, Abresch RT, Nicorici A, Elfring GL, Atkinson L, Reha A, Hirawat S, Miller LL. The 6-min walk test as a new outcome measure in Duchenne muscular dystrophy. Muscle Nerve. 2010; 41(4):500–10. https://doi.org/10.1002/mus.21544.PubMedCrossRef

10.

Lou J-S, Weiss MD, Carter GT. Assessment and Management of Fatigue in Neuromuscular Disease. Am J Hosp Palliat Med. 2010; 27(2):145–57. https://doi.org/10.1177/1049909109358420.CrossRef

11.

Mayhew A, Mazzone ES, Eagle M, Duong T, Ash M, Decostre V, Vandenhauwe M, Klingels K, Florence J, Main M, Bianco F, Henrikson E, Servais L, Campion G, Vroom E, Ricotti V, Goemans N, Mcdonald C, Mercuri E. Development of the Performance of the Upper Limb module for Duchenne muscular dystrophy. Dev Med Child Neurol. 2013; 55(11):1038–45. https://doi.org/10.1111/dmcn.12213.PubMedCrossRef

12.

Bushby K, Finkel R, Birnkrant DJ, Case LE, Clemens PR, Cripe L, Kaul A, Kinnett K, McDonald C, Pandya S, Poysky J, Shapiro F, Tomezsko J, Constantin C. Diagnosis and management of Duchenne muscular dystrophy, part 2: implementation of multidisciplinary care. Lancet Neurol. 2010; 9(2):177–89. https://doi.org/doi:10.1016/S1474-4422(09)70272-8.PubMedCrossRef

13.

Smets EM, Garssen B, Bonke B, De Haes JC. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res. 1995; 39(3):315–25.PubMedCrossRef

14.

Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989; 46(10):1121–3.PubMedCrossRef

15.

Wolfe F, Hawley DJ, Wilson K. The prevalence and meaning of fatigue in rheumatic disease,. J Rheumatol. 1996; 23(8):1407–17.PubMed

16.

Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech. 2009; 24(4):327–40. https://doi.org/10.1016/j.clinbiomech.2009.01.010.CrossRef

17.

Carr JC, Beck TW, Ye X, Wages NP. Intensity-dependent EMG response for the biceps brachii during sustained maximal and submaximal isometric contractions. Eur J Appl Physiol. 2016; 116(9):1747–55. https://doi.org/10.1007/s00421-016-3435-6.PubMedCrossRef

18.

Kahl L, Hofmann UG. Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals. Med Eng Phys. 2016; 38(11):1260–9. https://doi.org/10.1016/j.medengphy.2016.09.009.PubMedCrossRef

19.

González-Izal M, Malanda A, Navarro-Amézqueta I, Gorostiaga EM, Mallor F, Ibañez J, Izquierdo M. EMG spectral indices and muscle power fatigue during dynamic contractions. J Electromyogr Kinesiol. 2010; 20(2):233–40. https://doi.org/10.1016/j.jelekin.2009.03.011.PubMedCrossRef

20.

Hug F, Nordez A, Guével A. Can the electromyographic fatigue threshold be determined from superficial elbow flexor muscles during an isometric single-joint task?. Eur J Appl Physiol. 2009; 107(2):193–201. https://doi.org/10.1007/s00421-009-1114-6.PubMedCrossRef

21.

Oda S, Kida N. Neuromuscular fatigue during maximal concurrent hand grip and elbow flexion or extension. J Electromyogr Kinesiol. 2001; 11(4):281–9. https://doi.org/doi:10.1016/S1050-6411(01)00004-9.PubMedCrossRef

22.

Oliveira ASC, Gonçalves M, Cardozo AC, Barbosa FSS. Electromyographic fatigue threshold of the biceps brachii muscle during dynamic contraction. Electromyogr Clin Neurophysiol. 2005; 45(3):167–75.PubMed

23.

Ratel S, Duché P, Williams CA. Muscle Fatigue during High-Intensity Exercise in Children. Sports Med. 2006; 36(12):1031–65. https://doi.org/10.2165/00007256-200636120-00004.PubMedCrossRef

24.

Vøllestad NK. Measurement of human muscle fatigue. J Neurosci Methods. 1997; 74(2):219–27. https://doi.org/doi:10.1016/S0165-0270(97)02251-6.PubMedCrossRef

25.

Halin R, Germain P, Bercier S, Kapitaniak B, Buttelli O. Neuromuscular response of young boys versus men during sustained maximal contraction. Med Sci Sports Exerc. 2003; 35(6):1042–8. https://doi.org/10.1249/01.MSS.0000069407.02648.47.PubMedCrossRef

26.

Gregory CM, Bickel CS. Recruitment Patterns in Human Skeletal Muscle During Electrical Stimulation. Phys Ther. 2005; 85(4):358–64. https://doi.org/doi:10.1093/ptj/85.4.358.PubMed

27.

Naughton G, Carlson J, Fairweather I. Determining the Variability of Performance on Wingate Anaerobic Tests in Children Aged 6-12 Years. Int J Sports Med. 1992; 13(07):512–7. https://doi.org/10.1055/s-2007-1021308.PubMedCrossRef

28.

Marini F, Squeri V, Morasso P, Konczak J, Masia L. Robot-Aided Mapping of Wrist Proprioceptive Acuity across a 3D Workspace. PLOS ONE. 2016; 11(8):0161155. https://doi.org/10.1371/journal.pone.0161155.CrossRef

29.

Marini F, Squeri V, Morasso P, Campus C, Konczak J, Masia L. Robot-aided developmental assessment of wrist proprioception in children. J NeuroEngineering Rehabil. 2017; 14(1):3. https://doi.org/10.1186/s12984-016-0215-9.CrossRef

30.

De Santis D, Zenzeri J, Casadio M, Masia L, Morasso P, Squeri V. A new method for evaluating kinesthetic acuity during haptic interaction. Robotica. 2014; 32(08):1399–414. https://doi.org/10.1017/S0263574714002252.CrossRef

31.

Mugnosso M, Marini F, Gillardo M, Morasso P, Zenzeri J. A novel method for muscle fatigue assessment during robot-based tracking tasks. In: Proc. IEEE Int. Conf: Rehab. Robot (ICORR): 2017. p. 84–9. https://doi.org/10.1109/ICORR.2017.8009226.

32.

Oldfield RC. The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia. 1971; 9(1):97–113. https://doi.org/doi:10.1016/0028-3932(71)90067-4.PubMedCrossRef

33.

Masia L, Casadio M, Giannoni P, Sandini G, Morasso P. No Title. J NeuroEngineering Rehabil. 2009; 6(1):44. https://doi.org/10.1186/1743-0003-6-44.CrossRef

34.

Marini F, Contu S, Morasso P, Masia L, Zenzeri J. Codification mechanisms of wrist position sense. In: Proc. IEEE Int. Conf. Rehab. Robot (ICORR): 2017. p. 44–9. https://doi.org/10.1109/ICORR.2017.8009219.

35.

Phillips BA, Lo SK, Mastaglia FL. Muscle force measured using “break” testing with a hand-held myometer in normal subjects aged 20 to 69 years. Arch Phys Med Rehabil. 2000; 81(5):653–61. https://doi.org/doi:10.1016/S0003-9993(00)90050-9.PubMed

36.

Bäckman E, Johansson V, Häger B, Sjöblom P, Henriksson KG. Isometric muscle strength and muscular endurance in normal persons aged between 17 and 70 years. Scand J Rehabil Med. 1995; 27(2):109–17.PubMed

37.

Holmes MWR, Tat J, Keir PJ. Neuromechanical control of the forearm muscles during gripping with sudden flexion and extension wrist perturbations. Comput Methods Biomech Biomed Eng. 2015; 18(16):1826–34. https://doi.org/10.1080/10255842.2014.976811.CrossRef

38.

O’Malley M, Nolan P, LM. Electromyogram median frequency, spectral compression and muscle fibre conduction velocity during sustained sub-maximal contraction of the brachioradialis muscle. J Electromyogr Kinesiol. 2002; 12(2):111–8. https://doi.org/doi:10.1016/S1050-6411(02)00004-4.PubMedCrossRef

39.

Dimitrov G, Arabadzhiev T, Mileva K. Muscle fatigue during dynamic contractions assessed by new spectral indices. In: Medicine and science: 2006.

40.

Potvin JR, Bent LR. A validation of techniques using surface EMG signals from dynamic contractions to quantify muscle fatigue during repetitive tasks. J Electromyogr Kinesiol Off J Soc Electrophysiological Kinesiol. 1997; 7(2):131–9.CrossRef

41.

Knaflitz M, Merletti R, De Luca CJ. Inference of motor unit recruitment order in voluntary and electrically elicited contractions,. J Appl Physiol (Bethesda, Md. : 1985). 1990; 68(4):1657–67. https://doi.org/10.1152/jappl.1990.68.4.1657.CrossRef

42.

Gonzalez R, Buchananf T, Delp S. How muscle architecture and moment arms affect wrist flexion-extension moment. J Biomech. 1997; 30(7):705–12.PubMedCrossRef

43.

Merletti R, Knaflitz M, De Luca CJ. Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. J Appl Physiol. 1990; 69(5):1810–20.PubMedCrossRef

44.

Gerdle B, Henriksson-Larsén K, Lorenzton R, Wretling M-L. Dependence of the mean power frequency of the electromyogram on muscle force and fibre type. Acta Physiologica Scandinavica. 1991; 142(4):457–65. https://doi.org/10.1111/j.1748-1716.1991.tb09180.x.PubMedCrossRef

45.

Hggg ’ GM, Milerad2 E.Forearm extensor and flexor muscle exertion during simulated gripping work -an electromyographic study. Clin Biomech Clin Biomech. 1997; 1212(1):39–43.

46.

Mirakhorlo M, Visser JMA, Goislard de Monsabert BAAX, van der Helm FCT, Maas H, Veeger HEJ. Anatomical parameters for musculoskeletal modeling of the hand and wrist. Int Biomech. 2016; 3(1):40–9. https://doi.org/10.1080/23335432.2016.1191373.CrossRef

47.

Oberg T, Sandsjo L, Kadefors R, Öberg T, Sandsjö L, Kadefors R. Electromyogram mean power frequency in non-fatigued trapezius muscle. Eur J Appl Physiol Occup Physiol. 1990; 61(5-6):362–9. https://doi.org/10.1007/BF00236054.PubMedCrossRef

48.

Szucs K, Navalgund A, Borstad JD. Scapular muscle activation and co-activation following a fatigue task. Med Biol Eng Comput. 2009; 47(5):487–95. https://doi.org/10.1007/s11517-009-0485-5.PubMedCrossRef

49.

Croce RV, Miller JP. The effect of movement velocity and movement pattern on the reciprocal co-activation of the hamstrings. Electromyogr Clin Neurophysiol. 2003;43(8):451–8.

50.

Marbini A, Ferrari A, Cioni G, Bellanova MF, Fusco C, Gemignani F. Immunohistochemical study of muscle biopsy in children with cerebral palsy. Brain Dev. 2002; 24(2):63–6.PubMedCrossRef

51.

Madigan M, science in sports And PP-M. A muscle temperature compensation technique for EMG fatigue measures. 2002. undefined.europepmc.org.

52.

Gates DH, Dingwell JB. The effects of neuromuscular fatigue on task performance during repetitive goal-directed movements. Exp Brain Res. 2008; 187(4):573–85. https://doi.org/10.1007/s00221-008-1326-8.PubMedPubMedCentralCrossRef

53.

Lucidi CA, Lehman SL. Adaptation to fatigue of long duration in human wrist movements. J Appl Physiol (Bethesda, Md. : 1985). 1992; 73(6):2596–603.CrossRef

54.

Selen LPJ, Beek PJ, van Dieën JH. Fatigue-induced changes of impedance and performance in target tracking. Exp Brain Res. 2007; 181(1):99–108. https://doi.org/10.1007/s00221-007-0909-0.PubMedPubMedCentralCrossRef

55.

Abend W, Bizzi E, Morasso P. Human arm trajectory formation,. Brain. 1982; 105(Pt 2):331–48.PubMedCrossRef

56.

Octavia JR, Feys P, Coninx K. Development of Activity-Related Muscle Fatigue during Robot-Mediated Upper Limb Rehabilitation Training in Persons with Multiple Sclerosis: A Pilot Trial. Mult Scler Int. 2015; 2015:650431. https://doi.org/doi:10.1155/2015/650431.PubMedPubMedCentral

57.

Tooze JA, Schoeller DA, Subar AF, Kipnis V, Schatzkin A, Troiano RP. Total daily energy expenditure among middle-aged men and women: the OPEN Study,. Am J Clin Nutr. 2007; 86(2):382–7.PubMedCrossRef

Title: Muscle fatigue assessment during robot-mediated movements
Authors: Maddalena Mugnosso
Francesca Marini
Michael Holmes
Pietro Morasso
Jacopo Zenzeri
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: Journal of NeuroEngineering and Rehabilitation / Issue 1/2018
Electronic ISSN: 1743-0003
DOI: https://doi.org/10.1186/s12984-018-0463-y

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Muscle fatigue assessment during robot-mediated movements

Abstract

Background

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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