Gandevia, Simon C.

H\ue9roux, Martin

Muceli, Silvia

English

Chalmers Research

Signing up to motor signatures: a unique link to actionDownloaded from: https://research.chalmers.se, 2021-08-31 12:55 UTCCitation for the original published paper (version of record):Gandevia, S., Muceli, S., Héroux, M. (2019)Signing up to motor signatures: a unique link to actionJournal of Applied Physiology, 127(4): 1163-1164http://dx.doi.org/10.1152/japplphysiol.00643.2019N.B. When citing this work, cite the original published paper.research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology.It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004.research.chalmers.se is administrated and maintained by Chalmers Library(article starts on next page)EDITORIAL FOCUSSigning up to motor signatures: a unique link to actionSimon C. Gandevia,1,2 Silvia Muceli,3 and X Martin Héroux1,21Neuroscience Research Australia, Sydney, Australia; 2University of New South Wales, Sydney, Australia; and 3Division ofSignal Processing and Biomedical Engineering, Department of Electrical Engineering, Chalmers University of Technology,Gothenburg, SwedenSubmitted 16 September 2019; accepted in final form 17 September 2019We increasingly interact with the world via links that areunique to us. So we hear more about personalized medicine,personalized advertising, personalized training, and more.Such links are becoming more sophisticated and widespread intheir use. They range from our fingerprints, our facial and irisimages, our electrocardiogram (8), and of course our DNA(Fig. 1A). What about recognition based on our patterns ofmotion? We can recognize individuals from their gait—dothey limp? Are their hip movements symmetrical? Not surpris-ingly, algorithms can detect individuals from the forces andmovements they generate while walking (e.g., 3), forces andmovements that must result from patterns of activity in groupsof muscles. More than three decades ago we had reports thatindividuals differ in leg muscle activity in common tasks (1, 9).What was unclear was whether these differences were associ-ated with muscle activation patterns unique to each individual,another personalized biometric link.In a paper published in this issue of the Journal of AppliedPhysiology, Hug and colleagues (4) have explored the capacityof a machine learning approach (using support vector machineanalysis) to identify individuals from a group of 80 based onthe patterns of electromyographic activity (EMG) of eightmuscles in one leg. Their approach, which relied on thetemporal pattern of surface EMGs rather than their magnitude,was applied equally successfully to walking on a treadmill at1.1 m/s or pedaling on a cycle ergometer at different poweroutputs (up to 150 W and up to 15% maximum; 80 rpm). Aschematic representation of the approach is shown in Fig. 1B.The chosen tasks—walking and cycling—are of interest aswalking is arguably a more innate task than cycling (10),although both are likely to access a similar set of motor“modules” (2, 5). While considerable attention was given tocontrolling the motor tasks, the surface EMG electrodes wereplaced in standard locations and their position was not marked.Of the 80 participants, 53 returned for a second study.To recognize an individual from data collected in onesession, it was not necessary to use EMG activity from all eightmuscles; cyclic activity in only three muscles gave ~90%recognition in both pedaling and walking. Remarkably, thealgorithm was almost as accurate at recognizing individualswhen they repeated the pedaling and walking a couple ofweeks later. Here activity in six muscles gave accurate recog-nition in 80% of EMG cycles. In this way, muscle activationpatterns were unique motor signatures.The work leaves unanswered some basic questions: whatdoes it mean when some combinations of muscles performbetter than others? Compared with EMG patterns, how welldoes this form of machine learning perform when applied totask kinetics and kinematics? Would such kinetic and kine-matic signatures be linked to motor signatures? What can belearned from cycles that could not be correctly identified?Although there must be a nexus between profiles of muscleactivity and the forces needed to generate body motion, there isan envelope of solutions that allows for the individualityhighlighted here. Having said this, at what stage do our motorpatterns become unique? How distinct are motor patternsidentifiable in newborns, and how do they develop over timeand change based on the changing mechanics of the limbs andcharacteristics of their muscles (e.g., 6)?Finally, your signature’s idiosyncratic form is instantly rec-ognizable whether it is written with a pen on paper, with astylus on a tablet, or a brush on a billboard. This level ofuniqueness transcends even motor signatures of the type de-scribed by Hug and colleagues (4)—it represents a truly ad-vanced level of motor programming and personalization (7).Thus there remains much to learn about the generation, devel-opment, and malleability of motor “programs” from theirhigh-level selection right down to the motoneuronal output tothe muscles and the limits imposed by mechanical constraints.It is interesting to speculate about the impact of measuringthe uniqueness of muscle patterns in clinical and rehabilitationsettings—here the concept of individual signatures and thenew methods to measure them may be useful in identificationand treatment of pathological locomotor patterns.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the authors.AUTHOR CONTRIBUTIONSS.C.G., S.M., and M.H. drafted manuscript; S.C.G., S.M., and M.H. editedand revised manuscript; S.C.G., S.M., and M.H. approved final version ofmanuscript.REFERENCES1. Arsenault AB, Winter DA, Marteniuk RG. Is there a ‘normal’ profile ofEMG activity in gait? Med Biol Eng Comput 24: 337–343, 1986. doi:10.1007/BF02442685.2. Barroso FO, Torricelli D, Moreno JC, Taylor J, Gomez-Soriano J,Bravo-Esteban E, Piazza S, Santos C, Pons JL. Shared muscle synergiesin human walking and cycling. J Neurophysiol 112: 1984–1998, 2014.doi:10.1152/jn.00220.2014.Address for reprint requests and other correspondence: S. Gandevia, Neu-roscience Research Australia, 139 Barker St., Randwick, NSW 2031, Australia(e-mail: s.gandevia@neura.edu.au).J Appl Physiol 127: 1163–1164, 2019;First published September 26, 2019; doi:10.1152/japplphysiol.00643.2019.8750-7587/19 Copyright © 2019 the American Physiological Societyhttp://www.jappl.org 1163Downloaded from journals.physiology.org/journal/jappl at Chalmers Univ of Tech Lib (129.016.069.049) on April 1, 2021.3. Horst F, Mildner M, Schöllhorn WI. One-year persistence of individualgait patterns identified in a follow-up study - A call for individualiseddiagnose and therapy. Gait Posture 58: 476–480, 2017. doi:10.1016/j.gaitpost.2017.09.003.4. Hug F, Vogel C, Tucker K, Dorel S, Deschamps T, Le Carpentier E,Lacourpaille L. Individuals have unique muscle activation signatures asrevealed during gait and pedaling. J Appl Physiol (1985) 127: 1165–1174,2019. doi:10.1152/japplphysiol.01101.2018.5. Lacquaniti F, Ivanenko YP, Zago M. Patterned control of human locomo-tion. J Physiol 590: 2189–2199, 2012. doi:10.1113/jphysiol.2011.215137.6. Lacquaniti F, Ivanenko YP, Zago M. Development of human locomotion.Curr Opin Neurobiol 22: 822–828, 2012. doi:10.1016/j.conb.2012.03.012.7. Merton PA. How we control the contraction of our muscles. Sci Am 226:30–37, 1972. doi:10.1038/scientificamerican0572-30.8. Odinaka I, Lai P, Kaplan AD, O’Sullivan JA, Sirevaag EJ,Rohrbaugh JW. ECG biometric recognition: a comparative analysis.IEEE Trans Inf Foren Sec 7: 1812–1824, 2012. doi:10.1109/TIFS.2012.2215324.9. Pedotti A. A study of motor coordination and neuromuscular activitiesin human locomotion. Biol Cybern 26: 53–62, 1977. doi:10.1007/BF00363992.10. Srinivasan M, Ruina A. Computer optimization of a minimal bipedmodel discovers walking and running. Nature 439: 72–75, 2006. doi:10.1038/nature04113.step cycleA BFig. 1. A: depicts three ways by which a person canbe recognized: fingerprint, iris scan, and genomesequencing. B: shows schematically the procedureused by Hug and colleagues (4). Cyclic EMGs wererecorded from leg muscles during walking on atreadmill. The algorithm (based on support vectormachine analysis) was able to match successfullythe “signature” of EMGs from one step cycle withthose of an individual participant.1164J Appl Physiol • doi:10.1152/japplphysiol.00643.2019 • www.jappl.orgDownloaded from journals.physiology.org/journal/jappl at Chalmers Univ of Tech Lib (129.016.069.049) on April 1, 2021.

Signing up to motor signatures: a unique link to action

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Signing up to motor signatures: a unique link to action

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