Brouwer, Niels P.

Kingma, Idsart

Moya-Esteban, Alejandro

Sartori, Massimo

Tabasi, Ali

van Dieën, Jaap H.

van Dijk, Wietse

English

The effectiveness of exoskeletons could be enhanced by incorporating low back muscle fatigue estimates in their control. The aim of the present study was to evaluate whether low back muscle fatigue can be estimated using the spectral content of trunk extensor muscle high-density EMG (HDEMG) by considering the motor unit action potential conduction velocity (MUAP CV) as a reference. The HDEMG-based MUAP CV was estimated for multiple sites on the lower back consistently throughout a 30 degrees lumbar flexion endurance trial. Significant linear relationships were observed between MUAP CV and spectral content. However, MUAP CV and spectral changes over time did not show the expected decrease, probably due to additional recruitment of motor units or alternating activity of synergistic muscles. The anatomical information about the sites that allow MUAP CV estimation can be valuable for future low back muscle fatigue estimations.</p

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Can HDEMG-Based Low Back Muscle Fatigue Estimates Be Used in Exoskeleton Control During Prolonged Trunk Bending? A Pilot Study

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VU Research PortalCan HDEMG-Based Low Back Muscle Fatigue Estimates Be Used in ExoskeletonControl During Prolonged Trunk Bending? A Pilot StudyBrouwer, Niels P.; Tabasi, Ali; Moya-Esteban, Alejandro; Sartori, Massimo; van Dijk,Wietse; Kingma, Idsart; van Dieën, Jaap H.published inWearable Robotics: Challenges and Trends2022DOI (link to publisher)10.1007/978-3-030-69547-7_30document versionPublisher's PDF, also known as Version of recorddocument licenseArticle 25fa Dutch Copyright ActLink to publication in VU Research Portalcitation for published version (APA)Brouwer, N. P., Tabasi, A., Moya-Esteban, A., Sartori, M., van Dijk, W., Kingma, I., & van Dieën, J. H. (2022).Can HDEMG-Based Low Back Muscle Fatigue Estimates Be Used in Exoskeleton Control During ProlongedTrunk Bending? A Pilot Study. In J. C. Moreno, J. Masood, U. Schneider, C. Maufroy, & J. L. Pons (Eds.),Wearable Robotics: Challenges and Trends: Proceedings of the 5th International Symposium on WearableRobotics, WeRob2020, and of WearRAcon Europe 2020, October 13–16, 2020 (pp. 183-187). (Biosystems andBiorobotics; Vol. 27). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-69547-7_30General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 14. Oct. 2023Can HDEMG-Based Low Back MuscleFatigue Estimates Be Usedin Exoskeleton Control During ProlongedTrunk Bending? A Pilot StudyNiels P. Brouwer, Ali Tabasi, Alejandro Moya-Esteban, Massimo Sartori,Wietse van Dijk, Idsart Kingma, and Jaap H. van DieënAbstract The effectiveness of exoskeletons could be enhanced by incorporating lowback muscle fatigue estimates in their control. The aim of the present study was toevaluate whether low backmuscle fatigue can be estimated using the spectral contentof trunk extensor muscle high-density EMG (HDEMG) by considering the motorunit action potential conduction velocity (MUAP CV) as a reference. The HDEMG-based MUAP CV was estimated for multiple sites on the lower back consistentlythroughout a 30 degrees lumbar flexion endurance trial. Significant linear relation-ships were observed between MUAP CV and spectral content. However, MUAP CVand spectral changes over time did not show the expected decrease, probably due toadditional recruitment of motor units or alternating activity of synergistic muscles.The anatomical information about the sites that allow MUAP CV estimation can bevaluable for future low back muscle fatigue estimations.1 IntroductionExcessive cumulative lowback load, for instance due to prolonged static bending, anda limited trunk extensor muscle endurance are considered important risk factors forlow back pain [1, 2]. Hence, actuated trunk support exoskeletons would benefit fromincluding low backmuscle fatigue estimates in their control. Muscle fatigue has beenestimated previously in the lower back using the shift in spectral content of the EMG[3], which has been associated with low back muscle endurance time [4]. In otherN. P. Brouwer (B) · A. Tabasi · I. Kingma · J. H. van DieënDepartment of Human Movement Sciences, Vrije Universiteit Amsterdam, AmsterdamMovement Sciences, Amsterdam, The Netherlandse-mail: n.p.brouwer@vu.nlA. Moya-Esteban · M. SartoriDepartment of Biomechanical Engineering, University of Twente, Enschede, The NetherlandsW. van DijkTNO, Leiden, The Netherlands© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022J. C. Moreno et al. (eds.), Wearable Robotics: Challenges and Trends,Biosystems & Biorobotics 27, https://doi.org/10.1007/978-3-030-69547-7_30183184 N. P. Brouwer et al.muscles, such as the tibialis anterior muscle, these spectral changes have been asso-ciated with physiological manifestations of muscle fatigue, e.g. a decrease in motorunit action potential (MUAP) conduction velocity (CV) [5]. However, generalizingthe latter well-established association to the low back muscles may be complicated[6]. The muscle fatigue analysis based on the spectral content of low back muscleEMG is likely affected by (1) cross-talk of other back muscles; (2) complex musclearchitecture [6], e.g. varying distribution of motor-end plates and a large volume oftendinous tissue; and (3) potential local manifestations of muscle fatigue [7]. So, therelationship between the spectral content of low back muscle EMG and the MUAPCV may be questioned. The aim of the present study was to evaluate whether lowbackmuscle fatigue can be estimated using the spectral content of high-density EMG(HDEMG) measurements obtained at the low back, by relating it to the estimatedHDEMG-based MUAP CV as a muscle fatigue-related physiological reference [5].2 Material and MethodsOne male participant volunteered for the present study (age: 26 yr.; height: 1.75 m;body mass: 68.0 kg). At the time of the experiment, the participant did not have ahistory of low back pain. Four HDEMG 8x8 electrode grids with an 8.5mm inter-electrode distance were placed bilaterally 1cm from the spine (two grids per side),stacked with a 5mm inter-grid distance, starting from the height of the spinousprocess of L5. The low back muscle activity was recorded at 2048 Hz using the Refasystem (TMSi, Oldenzaal, The Netherlands). One inertial measurement unit (Xsens,Enschede, The Netherlands) was attached to the spine at the height of the spinousprocess of T10 to record the lumbar flexion angle. The participant was instructedto perform one maximum endurance effort at 30 ± 5 degrees lumbar flexion, whilethe pelvis was fixed in a frame. The 256 monopolar HDEMG signals were synchro-nized and band-pass filtered using a zero-lag second order Butterworth filter withcut-off frequencies 30–500Hz. A total of 192 double differential channels (48 pergrid) was constructed in cranial-caudal direction to reduce fibre-end potentials andother far-field potentials in the HDEMG signals [5]. These signals were interpolatedto increase the sample rate with a factor 16.Per pair of double differential channels, the MUAP CV was estimated in thecranial-caudal direction using an analysis on individual peaks [8]. For each signal,2s standard deviations were computed and peaks exceeding this windowed thresholdwere selected. Selected peaks were matched between channels considering a delayrange equal to infinite CV (i.e. no delay) down to 1 m s−1, allowing cranial andcaudal MUAP propagation. Pearson correlation coefficients were computed using15.6 ms windows constructed around matched peaks. Matched peaks with a Pearsoncorrelation (R) higher than 0.9 were selected for analysis [8]. Median CV, medianfrequency (MDF), mean frequency (MNF), and root-mean-square amplitude (RMS)were computed over 5s windows. MDF, MNF, and RMS time series were averagedper pair of double differential channels.A second order polynomialwas fitted throughCan HDEMG-Based Low Back Muscle Fatigue Estimates Be Used … 185Fig. 1 Relationship betweenchange in CV and change inMNF (R2 = 0.53, p =0.002). Red squares and bluecircles depict a measurementfrom the right and left side ofthe spine, respectivelythe median CV, spectral, and RMS time series. The root-mean-square error was usedto evaluate the quality of the fit and CV estimations with a median CV fit exceedinga root-mean-square error of 0.2 were excluded. Linear regression was employedto evaluate the relationship between the change (i.e. difference between initial andfinal value of the fit) in CV and the change in MDF, MNF, and RMS across doubledifferential channel locations.3 ResultsEndurance time was 785s. The peak velocity analysis on the bottom two grids didnot reveal consistent travelling MUAPs. The analysis on the top two grids revealedtravellingMUAPs in caudal direction consistently throughout the trial betweenfifteen(5 right, 10 left) pairs of double differential channels. However, unexpectedly, 8 outof the 15 showed an increase of MUAP CV, MDF and MNF. The initial values ofthe median CV fit ranged from 2.05 to 4.27 m s−1 (m ± sd: 3.14 ± 0.85 m s−1).Linear regression revealed significant relationships between changes over time ofCV and both spectral measures (MDF: R2 = 0.38, p= 0.015; MNF: R2 = 0.53, p=0.002; see Fig. 1), but no significant relationship between the CV and RMS changeover time. All averaged RMS signals increased throughout the trial (m ± std: 57.7± 17.2%).4 DiscussionThe aim of the present study was to evaluate whether trunk extensor muscle fatiguecan be estimated using the spectral content of HDEMG, considering the estimatedHDEMG-based MUAP CV as a reference. In contrast to previous work [6], weestimated the MUAP CV at a total of 15 sites. These sites were at the lateral endof both grids (approximately at the spinal level of T12-L2). Three of the CV timeseries showed a decrease over time, indicating the development of muscle fatigue.186 N. P. Brouwer et al.However, the majority of the CV time series showed an increase, and this coincidedwith an increase in frequency content (Fig. 1). Asmight be supported by the observedincrease in amplitude for all channels, this could be related to additional recruitmentof larger, faster and more superficial motor units due to the reduced force capacityof the already recruited and fatigued motor units [6]. Additionally, this could alsobe related to alternating activity of synergistic trunk extensor muscles [7]. Bothmanifestations may have masked fatigue related spectral and CV changes.The bottom two grids and medial side of the upper grids yielded no successfulMUAP CV estimates, probably due to the complex low back muscle architecture.Placement of electrodes close to the innervation zones or fibre-ends of musclescan affect the CV estimates and result in additional high frequency content [9].Nevertheless, the results of the present study indicate a potential anatomical loca-tion where the CV and low back muscle fatigue can be estimated using HDEMG.Future studies should evaluate the consistency of the present finding regarding theanatomical location of the CV estimates across subjects.5 ConclusionIn the present pilot study, we estimated the trunk extensorMUAPCV using HDEMGfor multiple sites consistently throughout a 30 degrees lumbar flexion endurancetrial. Hence, the relationship between the CV and shift of the corresponding spectralcontent could be evaluated for the trunk extensor muscles. However, estimating trunkextensor muscle fatigue based on CV or surface EMG spectral content appears notfeasible yet, possibly due to the additional recruitment of motor units or alternatingactivity of synergistic muscles.Acknowledgements This work was supported by the Dutch Research Council (NWO), program‘perspectief’ (project P16-05).References1. P. Coenen, I. Kingma et al., Cumulative low back load at work as a risk factor of low back pain:a prospective cohort study. J. Occup. Rehabil. 23(1), 11–18 (2013)2. W.E. Hoogendoorn, P.M. Bongers, et al., Flexion and rotation of the trunk and lifting at workare risk factors for low back pain. Spine (Phila. Pa. 1976), 25(23), 3087–3092 (2000)3. J.H. van Dieën, E.P. Westebring-van der Putten et al., Low-level activity of the trunkextensor muscles causes electromyographic manifestations of fatigue in absence of decreasedoxygenation. J. Electromyogr. Kinesiol. 19(3), 398–406 (2009)4. J.H. van Dieën, H.H.E. Oude Vrielink, et al., Trunk extensor endurance and its relationship toelectromyogram parameters. Eur. J. Appl. Physiol. 66(5), 388–396 (1993)5. R.Merletti, M. Knaflitz et al., Myoelectric manifestations of fatigue in voluntary and electricallyelicited contractions. J. Appl. Physiol. 69(5), 1810–1820 (1990)Can HDEMG-Based Low Back Muscle Fatigue Estimates Be Used … 1876. D. Farina, M. Gazzoni et al., Assessment of low back muscle fatigue by surface EMG signalanalysis: Methodological aspects. J. Electromyogr. Kinesiol. 13(4), 319–332 (2003)7. J.H. Van Dieën, H.H.E. Oude Vrielink, et al., An investigation into the relevance of the patternof temporal activation with respect to erector spinae muscle endurance. Eur. J. Appl. Physiol.66(1), 70–75 (1993)8. R.B.J. Beck, C.J. Houtman et al., A technique to track individual motor unit action potentials insurface EMG by monitoring their conduction velocities and amplitudes. IEEE Trans. Biomed.Eng. 52(4), 622–629 (2005)9. J.Y. Hogrel, J. Duchêne et al., Variability of some SEMG parameter estimates with electrodelocation. J. Electromyogr. Kinesiol. 8(5), 305–315 (1998)

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Can HDEMG-Based Low Back Muscle Fatigue Estimates Be Used in Exoskeleton Control During Prolonged Trunk Bending? A Pilot Study

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