To analyse the spectral density of electromyographic (EMG) signals Fourier transforms are commonly used. The prerequisite of this transform is that the analysed signal is stationary. Generally, this can not be assumed for the electromyograms of muscle contractions of human movement. A new method to analyse non-stationary biological signals is the time-variant spectral analysis. The aim of this paper is to use the timevariant spectral analysis in a realistic sport application to show connections of the athlete’s level and the spectral density of the EMG. Five top-level archers participated in the study. The results suggest, that a higher level of performance generally corresponds to lower median-frequencies and a smaller variability of the median-frequencies of the EMG-signals

Edelmann-Nusser, J.

Schack, B.

Witte, K.

English

ISBS (International Society of Biomechanics in Sports): Conference Proceedings Archive

Biomechanics Symposia 2001 / University of San Francisco166TIME-VARIANT SPECTRAL ANALYSIS OF SURFACE EMG SIGNALS – EXEMPLARILYSHOWN FOR ARCHERYJürgen Edelmann-Nusser, Kertin Witte, and Bärbel Schack1Department of Sport Science, Otto-von-Guericke-University Magdeburg, Germany1Institute of Medical Statistics and Computer Sciences, University of Jena, GermanyTo analyse the spectral density of electromyographic (EMG) signals Fourier transformsare commonly used. The prerequisite of this transform is that the analysed signal isstationary. Generally, this can not be assumed for the electromyograms of musclecontractions of human movement. A new method to analyse non-stationary biologicalsignals is the time-variant spectral analysis. The aim of this paper is to use the time-variant spectral analysis in a realistic sport application to show connections of theathlete’s level and the spectral density of the EMG. Five top-level archers participated inthe study. The results suggest, that a higher level of performance generally correspondsto lower median-frequencies and a smaller variability of the median-frequencies of theEMG-signals.KEY WORDS: non-stationary signals, ARMA model, Fourier transform, frequency-time                         analysisINTRODUCTION: Most spectral analyses of biological signals such as EMG or EEG base onFourier transforms. But owing to the non-stationary behavior of real biological signals thismethod may not always be appropriate since Fourier transforms require stationary signals.Using two dimensional frequency-time analyses a second problem of Fourier transforms is therange of the sliding window: If the range is large the time resolution is poor, if the range is smallthe time resolution is high but the computed spectral density is poor. A new method to do frequency-time analyses of non-stationary biological signals is the time-variant spectral analysis. The aim of this paper is to demonstrate the use of the time-variant spectral analysis in serioussport and to show that there are connections of the athletes’ levels and the spectral density ofthe EMG even in highly skilled athletes. This is exemplarily shown for archery. Archery is used,because it is a very precise movement that can be repeated very often without showing anyfatigue and the performance can be quantified by the score. METHODS: The time-variant spectral analysis is able to react on structure changes of the non-stationary signal. This condition is fulfilled by an autoregressive moving average model (ARMAmodel)  with time variant parameters: The parameters are changed by an adaptive estimationprocedure at every sample point in a manner minimizing the error of the model (see Schack et.al., 1995; Schack et al. 1996). In archery immediately before the shot there are changes in the athlete’s muscle activity ofshoulder and back. From a biomechanical point of view, the archer has to cope with the releaseof the static balance of forces between the external tension and the muscular forces (see Fig. 1)at the time of shooting by means of his neuromuscular system. The EMGs of eight muscles (on the pull side: m. extensor carpi radialis brevis, m. flexor carpiradialis, m. deltoideus, m. trapezius pars descendens-transversa, m. trapezius pars transversaand m. biceps brachii; on the bow side: m. pectoralis major and m. deltoideus) of each archerwere acquired bipolary at a sampling rate of 2000 Hz. The muscles were selected according tothe literature (Squadrone, & Rodano, 1994) and on reasons of plausibility. The moment of therelease of the archer’s hand from the bowstring and the contact-loss of the arrow with thebowstring were acquired using an accelerometer that was fixed to any position of the riser (seeEdelmann-Nusser, & Gruber, 2000). Five female archers of different performance levels of the German National Team participated inthis study:  One medalist in the Olympic Games 1996 and 2000 (level A), three participants ofworld championships but no medalists (level B) and one participant of national championshipsBiomechanics Symposia 2001 / University of San Francisco167(level C). Each archer shot with his own personal bow 20 times over a distance of 18m. Thearchers were instructed to aspire a maximum score.Figure 1 - Schematic diagram of an archer’s body segments shown from the top. Thebow applies forces to the hand at the bow’s side and the hand of the pull side. Thearcher's muscles produce equal and opposite forces resulting in a static balance offorces. Hence, there are torques in the joints (circles in the Figure). When the handreleases the bowstring, the force F as well as the torques decrease immediately to zero,the static balance of the forces breaks down. To anticipate this, neuromuscularactivities are necessary immediately before the static balance breaks down.RESULTS: The eight muscles showed interindividually quite different activation patterns andchanges in the activity with the exception of the m. trapezius pars transversa. Therefore only theEMGs of the m. trapezii partes transversae were researched: For all archers a decreasingactivity of the m. trapezius pars transversa was observed immediately before the release of thehand from the bowstring (see Figure 2). Figure 2 - Typical rectified EMG of the m. trapezius pars transversa immediately beforethe shot. The moment of the release of the archer’s hand from the bowstring (thand) andthe contact-loss of the arrow with the bowstring are detected by an accelerometer (signal“a-bow”). Table 1 shows the archers’ mean scores. As expected the mean scores of the archers BA, SA,TI and NI correspond to their level. But although archer SB is level B she has the lowest score.This may be caused by an injury: she could not train for three months and resumed training onlytwo weeks prior to the test. Table 1   The Archers’ Mean Scores  (The highest mean score that is possible is 10.) Archer BA, level A SA, level B TI, level B NI, level C SB, level BMean score 9,43 9,29 9,13 8,73 7,90Biomechanics Symposia 2001 / University of San FranciscoFor each archer and every shot the time courtime course of the median frequency was coFigure 4 shows the mean median frequencies standard deviation of the median frequencies oTable 2 shows the mean median frequencies level A archer. It is evident that the mean medthe mean median frequencies of the other arstandard deviation of the median frequency odeviations of the median frequencies of the omean median frequency of the level C archer iother archers with the exception of the injureddeviation of the median frequency of the level the median frequencies of the other archers wFigure 3 - Time course of the frequency band of one shot of archer NI and one shotof archer BA. To the left of each frequency band the rectified EMG is drawn in. It isevident that the band width of NI is larger and that higher frequencies can beobserved.0 200 400 60010152025Contact-loss ofthe arrow withthe bowstring BA (A) NI (C) SA (B)sd [Hz]Time [ms]Figure 5 - Standard deviation of median                  frequencies of 3 archers.0 200 400 6004050607080 Contact-loss ofthe arrow withthe bowstring BA (A) Ni (C) SA (B)Mean Median Frequency [Hz]Time [ms]Figure 4 - Mean of median frequencies of 3                  archers.168se of the frequency band (see Figure 3) and themputed using the time-variant spectral analysis.of every shot of three archers, Figure 5 shows thef every shot of three archers. of the level B and C archers in comparison to theian frequency of the level A archer is lower thanchers. As Table 4 shows, it also evident that thef the level A archer is smaller than the standardther archers.  Vice versa Table 3 shows that thes higher than the mean median frequencies of the archer SB and Table 5 shows that the standardC archer is larger than the standard deviations ofith the exception of the injured archer SB.  HenceBiomechanics Symposia 2001 / University of San Francisco169the results show clearly that in the researched highly skilled archers a higher level ofperformance corresponds to a lower median frequency of the EMG and a smaller variability ofthe median frequency of the EMG.  Table 2  Percentages of Higher Mean Median Frequencies of Level B and CArchers (NI, SA, SB, TI) Compared to the Level A Archer (BA)NI-BA SA-BA SB-BA TI-BAPercentage 100% 94,7% 100% 70,8%Table 3  Percentages of Lower Mean Median Frequencies of Level A and B Archers(BA, SA, SB, TI) Compared to the Level C Archer (NI)  Please note, that SB islevel B but her score is lower than NI’s score (compare Table 1).NI-BA NI-SA NI-TI NI-SBPercentage 100% 63% 91,7% 6,75%Table 4  Percentages of Higher Standard Deviations of the Median Frequencies ofLevel B and C Archers (NI, SA, SB, TI) Compared to the Level A Archer (BA)NI-BA SA-BA SB-BA TI-BAPercentage 95,3% 73,2% 96,4% 86,2%Table 5: Percentages of Lower Standard Deviations of Median Frequencies ofLevel A and B Archers (BA, SA, SB, TI) Compared to the Level C Archer (NI)NI-BA NI-SA NI-TI NI-SBPercentage 95,3% 86,7% 68,8% 37,1%CONCLUSION: The results suggest that the time-variant spectral analysis is a useful method toanalyze the spectral density of surface electromyograms in serious sport: For even in highskilled athletes differences in the performance level can be quantified in the spectral density ofthe surface-EMG signals, the time-variant spectral analysis could be used in training processesto quantify an in- or decrease of the performance level. The poor score of archer SB can be interpreted in two alternative ways:1. Less training leads to higher median frequencies which means that there is no long termstability of the EMG frequency pattern of high trained skills. Or:2. The coordination of the athlete has changed because of her injury and she has to train thisnew coordination.To decide this further research is necessary.REFERENCES:Edelmann-Nusser, J., & Gruber, M. (2000). Release of the Force Balance in Highly SkilledArchers: Connections of the Bow's Motion with EMG Data. In: Subic, A. J., & Haake, S. J.(Eds.), The Engineering of Sport (pp 299-307). Oxford: Blackwell Science.Schack, B., Griessbach, G., Bareshova, E., Anders, CH., Schumann, N.-P., & Florian, G.(1996). Parametric methods of dynamic spectral analysis of non-stationary biological signals.Medical & Biological Engineering & Computing, 33, 605-610.Schack, B., Bareshova, E., Griessbach, G., & Witte, H. (1995). Methods of Dynamic SpectralAnalysis by Self-exciting ARMA Models and their Application to Analysing Biosignals. Med. &Biol. Eng. & Comput., 33, 492-498.Squadrone, R., & Rodano, R.(1994). Multifactorial Analysis of Shooting Archery. In: Barabas,A., & Fabian, G. (Eds.), Proceedings of the 12th ISBS Symposium.ACKNOWLEDGEMENTS:This study was founded by the Federal Institute of Sports Science (Bundesinstitut fürSportwissenschaft; reference no.: VF 0407/05/02/95), Cologne, Germany. 

TIME-VARIANT SPECTRAL ANALYSIS OF SURFACE EMG SIGNALS – EXEMPLARILY SHOWN FOR ARCHERY

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TIME-VARIANT SPECTRAL ANALYSIS OF SURFACE EMG SIGNALS – EXEMPLARILY SHOWN FOR ARCHERY

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