- Email: [email protected]
EMG signals detection and processing for on-line control of functional electrical stimulation
Journal of Electromyography and Kinesiology 10 (2000) 351–360 www.elsevier.com/locate/jelekin
EMG signals detection and processing for on-line control of functional electrical stimulation C. Frigo a
a,*
, M. Ferrarin a, W. Frasson a, E. Pavan a, R. Thorsen
b
Centro di Bioingegneria, Fnd. Don Gnocchi I.R.C.C.S.—Politecnico di Milano, Milan, Italy b Medical Physics and Bioengineering, University College London, London, UK
Abstract The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artefact and the non-voluntary contribution (M-wave). Six orientations and six locations of the recording electrodes were investigated in the present work. A comb filter (with and without a blanking windowing) was applied to remove the signal components synchronously correlated to the stimulus. An operative definition of the signal to noise ratio and an efficiency index were implemented. It resulted that when the recording electrodes were located within the two stimulation electrodes the best orientation was perpendicular to the longitudinal line. However the best absolute indexes were obtained when the recording electrodes were located externally of the stimulation electrodes, and in that case the best orientation was longitudinal. Concerning the filtering procedure, the use of a blanking window before the application of the comb filter, gave the best performance. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Surface EMG; FES control; Electrode placement; EMG signal processing
1. Introduction Surface Electromyogram (EMG) may be aimed at many different objectives. For example the extraction of features related to patterns of movement [6,15– 17,21,22]; the analysis of the force–EMG relationship in particular conditions [1,3,9]; the co-ordination of muscles activity in a particular motor task [3,15,21]. In other cases the objective is to identify signs of muscle degeneration or abnormal behaviour [6,11]. Muscle fatigue and changes in the Motor Unit Action Potential (MUAPT) propagation mechanism can also be analysed through proper modelling and use of statistical indicators [8,12]. In most of these applications the signal recording conditions can be (and must be) controlled quite carefully. Isometric–isotonic conditions are in general required to avoid motion artefacts, to insure that recording is made from a pre-defined portion of the muscle, to guarantee
* Corresponding author. Tel.: +39-02-40308305; fax: +39-024048919. E-mail address: [email protected] (C. Frigo).
signal stationarity, to avoid non linearities connected to changes in muscle geometry and to movements. When muscle co-ordination is the objective of the study, and the surface EMG signal is detected in dynamic conditions, all the above sources of artefact and non linearities are likely to affect the signal. Electrode size and arrangement, signal conditioning, and processing algorithms have to be properly selected and optimised in an attempt to reduce these detrimental effects and to improve the quality of the signal [2,12]. Detection of surface EMG signals in the presence of electrical stimulation presents additional difficulties because the signals delivered by the stimulation electrodes can be much higher than the signals generated by the muscles, and thus can saturate the amplifiers for a considerable time. Many examples can be found in literature [4,5,7,10,18] where different approaches have been used to reject stimulation artefacts. However it’s relatively recent [13,14,19] that these techniques have been applied in a control scheme for Functional Electrical Stimulation (FES). One of the projects our group is working on concerns the use of volitional EMG signals to modulate the stimulation strength delivered to paralysed muscles. A particular application (here named
1050-6411/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 1 0 5 0 - 6 4 1 1 ( 0 0 ) 0 0 0 2 6 - 2
352
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
‘homologous stimulation’) is that of detecting the residual activity of a partially paralysed muscle and using it to control the stimulation of the muscle itself. This is the worst operative condition since the stimulation artefact is the dominant component in the recorded signal. One of the authors of this paper originally designed the first prototype of a portable device (MeCFES— Myoelectrically Controlled Functional Electrical Stimulator) that included one channel of EMG recording, one channel of stimulation, and a Digital Signal Processor (DSP). It was designed for the recovery of hand grasping [19]. A new prototype (MuMeCS—Multichannel Myoelectrically Controlled Stimulator) has been subsequently designed for the application of surface FES to lower limbs, with the purpose of recovering standing up, sitting down, and walking with external support. Some techniques of stimulation artefact reduction have been incorporated in the hardware design, and a special filtering procedure has been implemented in the DSP. Further enhancement of the signal to noise ratio can be obtained through proper positioning of the recording electrodes in relation to the stimulation electrodes. This paper presents the effects of different electrodes arrangements and filtering procedures on the capability of the system to extract the volitional component from the artefacted EMG signal. The aim of the study was to define the criteria for an optimal compromise between artefact rejection and voluntary muscle activity detection.
2. Methods 2.1. The multichannel myoelectrically controlled stimulator (MuMeCS) device description Fig. 1(A) shows the scheme of the MuMeCS portable device. It consisted of two integrated parts: the 4 channels EMG recording unit (differential amplifiers, gain 5000, input impedance 10 G⍀) and the 4 channels stimulation unit (bi-phasic wave shapes with neutral interpulse interval, total duration in the range 20–900 µs, 5– 100 Hz frequency range, 0–100 mA current range). Four analogic signals (0–3 V) could also be collected from additional sensor (foot switches, electrogoniometres) and used in the stimulation control strategy. All the input signals were sampled at 1 kHz, converted into a digital form by an analog to digital (A/D) 12 bits converter, and sent to the Digital Signal Processor (DSP). A digital to analog (D/A) 10 bits converter provided the signal to the stimulation unit which in turn controlled the current delivered to the isolation unit (electric transformer). Due to this control scheme the current delivered to the subject was load independent. The electric transformer also guarantees that no net electrical charge was injected into the patient.
The stimulation artefacts reduction relied on the following features: (a) fast recovery from input overloads, presented by the instrumentation amplifiers [20] (b) automatic compensation of DC-offset fluctuations by a non-linear feedback loop [20] (c) low stimulator output impedance, by means of the electric transformer, at a value that is one dimension order less than patient’s skin impedance When the recording electrodes are relatively close to the stimulation electrodes the above features are not sufficient for a complete rejection of the stimulus artefact. Fig. 2 shows some examples of signals recorded from different muscles in the absence of voluntary contraction and during electrical stimulation of the rectus femoris muscle. The stimulation pattern consisted of a positive and a negative pulse of 0.3 ms duration each, separated by a neutral inter-pulse interval of 0.3 ms. Pulse amplitude was 40 mA and stimulation frequency was 16.67 Hz. The surface stimulation electrodes (5×9 cm) were aligned longitudinally to the muscle at an inter-electrode distance of 16 cm. The recording surface electrodes (0.8 cm ⭋) were positioned on the muscle bellies, slightly displaced towards the tendon extremity, 3 cm apart, and aligned with the muscle fibres. In this experiment the ground electrode was positioned, according to usual practice, on the wrist. A high pass filter (10 Hz, to avoid base line oscillations) and the anti-aliasing low-pass active filter shown in Fig. 1 (500 Hz, Bessel, 2nd order) were applied on the EMG signals before data sampling. It clearly appears that the stimulus artefact is strongly detected by the electrodes closer to the stimulation site. In addition, the muscle response to the stimulus (Mwave) is present on the stimulated muscle and on the synergistic ones (vastus lateralis). 2.2. The experimental conditions The effect of different orientations and locations of the recording electrodes on the signal recorded in the presence of homologous electrical stimulation was investigated systematically on a healthy subject. The electrical stimulation was delivered onto the rectus femoris muscle of the left leg, with stimulation electrodes positioned as described above. The subject seated on a bench with the back supported by a backrest at 60°. The hip was thus flexed by 60°. The ankle of the testing leg was fastened to a rigid structure that maintained the knee flexed at about 45° and allowed measuring the force (through a strain gauge cell) generated at the ankle by the knee extensor muscles. The tests were conducted as follows (see Fig. 3): 1. The subject was asked to perform a Maximum Volun-
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
353
Fig. 1. (A) Schematic representation of the MuMeCS device. The Digital Signal Processor (DSP) receives the differentially amplified and prefiltered signals from the analog to digital (A/D) converter, processes the data on line and sends a stimulation control signal to the digital to analog (D/A) converter. A personal computer (PC) can interact with the DSP during the testing and set up phases. The stimulation unit is galvanically isolated from the patient through a low impedance transformer. (B) Recording electrodes arrangement on a hexagonal plate. (C) Schematic view of the considered placements of the recording electrodes in relation to the stimulation electrodes on the left thigh.
tary Contraction (MVC) for about 4 s, and then, under visual feedback of the force produced at the ankle, to reduce the contraction to 40% MVC for 4 s and then to relax. 2. An electrical stimulation producing 40% MVC was then applied for 6 s. After 2 s from the stimulation onset the subject was asked to increase the force at the ankle by an additional 40% MVC through a voluntary activation of the knee extensor muscles. Therefore, in this phase a total of about 80% MVC was achieved. After 4 s the electrical stimulation was switched off while the subject maintained the voluntary contraction for additional 4 s at 40% MVC. Stimulation parameters were the same as in the experiment described previously apart from stimulation intensity that was adjusted to obtain the desired level of muscle contraction. In this experiment two stimulation
frequencies were considered: 16.67 and 25 Hz. These values were chosen because they fall in the range that is usually adopted for neuromuscular stimulation, and are submultiples of the power line frequency (in Europe 50 Hz). This allowed us to reduce the stimulation component and all its multiple frequencies, including the power line frequency, by applying a single comb filter (see below). At 16.67 Hz stimulation frequency the 40% of MVC was obtained with a current of about 60 mA, while at 25 Hz only 50 mA were required. The recording electrodes were arranged in three bipolar couples and mounted on a hexagonal plate. The relative orientation was spaced by 60° [Fig. 1(B)]. This allowed us to test the following orientations with respect to the stimulation electrodes: 90°, +30°, ⫺30°. The distance between the two electrodes in each couple was 5 cm to allow the ground reference electrode to be located in the centre of the plate.
354
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
Fig. 2. Signals recorded on different muscle locations during electrical stimulation of rectus femoris (I=40 mA, f=16.67 Hz). In (A) the signal was recorded on the triceps brachialis muscle, in (B) on the erector spinae; in (C) on the gluteus medius; in (D) on the medial hamstrings; in (E) on the vastus lateralis, and in (F) it was on the same stimulated muscle (rectus femoris). The amplitude scale is in mV. Note that the graphs on the left side are 10× magnified with respect to those on the right. Signals were only pre-processed by the adaptive gain amplifiers and Bessel antialiasing filter.
The electrodes mounting plate was placed in the following locations [see Fig. 1(C)]: 1. in the middle between the two stimulation electrodes 2. proximal to the stimulation electrodes (still in line with them) 3. in between the two stimulation electrodes but 4 cm medially 4. as in (3) but 4 cm laterally 5. distal–medial to the stimulation electrodes
6. distal–lateral to the stimulation electrodes For location 1 and 2 the hexagonal plate was also rotated by 30°, in order to analyse the following three additional orientations: +60°, 0°, ⫺60°. 2.3. Signal processing and performance indexes The technique adopted for extracting the voluntary EMG component from the raw artefacted signal was based on the use of a ‘comb’ filter.
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
355
Fig. 3. Example of a recording trial on a healthy subject. In (A) the raw EMG signal is reported as detected from the rectus femoris by electrodes located proximally to the stimulation electrodes (placement 2), and rotated ⫺30° in relation to the longitudinal line. Voluntary contraction about 100% and 40% MVC was produced in a first phase (see the force pattern in D). At time 13 s the electrical stimulation started and lasted 6 s (heavy horizontal bar in section AD). The force produced was approximately 40% MVC. At time 15 s a voluntary activation was elicited and this produced an additional force of 40% MVC. At time 19 s the electrical stimulation was switched off, while the subject exerted the same voluntary force as before (40% MVC). At time 24 s the voluntary force was also terminated. In (B) the signal extracted by the comb filter applied to the EMG signal of (A) is reported; in (C) the RMS of signal (B) for each inter-stimulus interval is shown.
This filter is a finite impulse response (FIR) filter having the following expression: y(n)⫽
x(n)−x(n−NTstm)
冑2
(1)
where: x(n) NTstim y(n) √2
is the raw EMG signal at sampling time n is the inter-stimulus time expressed in number of samples is the filtered EMG signal is a scale factor required to keep the same power in the signal before and after filtering
Two cases were tested and compared: (a) the above comb filter was applied to the whole recorded signal (b) a blanking window of 20 ms (during which the signal was zeroed) was applied after each stimulation pulse, and then the comb filter was applied to the residual time window. The blanking window was aimed at directly rejecting the portion of the signal where the stimulus artefact and the M-wave were likely to occur. The amplitude of the processed signal was quantified through its RMS value. The latter was computed for every stimulus (i), by the following expression:
356
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
冪冘
RMS(i)⫽
1 N
N
yi(n)2
(2)
1
where yi(n) N
is the nth sample of the filtered EMG signal within each post-stimulus interval (not considering the blanking period) is the number of samples considered within the post-stimulus interval.
Fig. 3(C) shows the time course of the RMS value in correspondence with the different contraction phases. In the stationary part of each phase the average RMS value was computed. Let’s call: Yvol Ystim Ystim+vol
the average RMS value in the period (T3) when a 40% MVC was produced voluntarily the average RMS in the period (T1) when the same force level was obtained by the electrical stimulation alone the average RMS in the period (T2) when a 40% of MVC was superimposed to an electrically induced contraction at a force level of 40% MVC.
Theoretically, Ystim should be zero since no voluntary component is present in T1, but, as matter of fact, residual noise and not completely removed artefacts can still be present. Provided that the same stimulation strength is delivered, we can assume that Ystim holds the same value even in the presence of voluntary activity. Therefore, the contribution to Ystim+vol due to the voluntary contraction can be estimated as the difference between Ystim+vol and Ystim. According to our objective, for a given level of voluntary contraction superimposed to the electrical stimulation, an optimal combination of electrodes arrangement and filtering procedure should be able to detect all the voluntary EMG component and to reject all the components associated to the stimulus artefact. We defined an Operative Signal to Noise Ratio (OSNR) with the purpose of quantifying this capability: OSNR⫽
Ystim+vol−Ystim Ystim
Yvol Ystim
OSNR Ystim+vol−Ystim ⫽ EI⫽ VSNR Yvol
(5)
This index represents the capability of a given recording and processing condition to extract the voluntary command from the EMG signal in presence of stimulation, relatively to the same condition without stimulation. EI normally ranges from 0 (no voluntary signal detection), to 1 (no degradation due to stimulation). The results will focus mainly on OSNR, since it allows to compare different configurations and to find which of them one optimises the detection of a voluntary signal in the presence of electrical stimulation.
3. Results Figs. 4 and 5 report the Operative Signal to Noise Ratios (OSNR) obtained with different electrodes placements (1–6, see Fig. 1), electrodes orientations in relation to the stimulating electrodes (90° to ⫺60°), filtering procedures (comb filter alone and a 20 ms blanking in addition to the comb filter), and stimulation frequencies (16.67 and 25 Hz). The effects of all these different conditions will be analysed in the following. 3.1. Stimulation frequency For almost all the recording and processing conditions the highest values of the OSNR were obtained at a stimulation frequency of 25 Hz. In some cases the relative amplitude of the OSNR among the different electrodes placements changed depending on the stimulation frequency. For example the highest value was obtained for electrode placement 2, orientation 0° at 25 Hz. The same electrodes location and orientation yield the lower OSNR at a stimulation frequency of 16.67 Hz. Also, the electrode placement 1 proved to be sensitive to the different stimulation frequencies, as the relative amplitude obtained for the different electrodes orientations changed.
(3) 3.2. Filtering procedure
A Virtual Signal to Noise Ratio can instead be defined by considering the voluntary EMG signal measured in phase T3, when stimulation is not present: VSNR⫽
lower than VSNR. To quantify the degradation of the performance when stimulation is active, the following Efficiency Index (EI) was therefore defined:
(4)
Because of the interaction between voluntary and stimulation components, it is expected that OSNR is in general
The comb filter proved to be considerably efficient in reducing the stimulus artefact and enhancing the voluntary component contribution. In fact the OSNR computed after filtering was in general more than one order of magnitude higher than the OSNR computed on the raw signals. In some cases the improvement was dramatic. For example, the OSNR computed on the raw sig-
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
357
Fig. 4. OSNRs for different recording electrode arrangements and filtering procedures. Stimulation on rectus femoris with a stimulation frequency of 16.67 Hz. Above the stimulus artefact were rejected by the comb filter; below the additional 20 ms blanking window was applied prior to the application of the comb filter. Each set of vertical bars correspond to different placements of the electrodes [the numbering is according to Fig. 1(C)]: (1) centered; (2) proximal, (3) centered medial; (4) centered lateral; (5) distal medial; (6) distal lateral. Each vertical bar corresponds to a different orientation of the recording electrodes. From left to right, in progression: 90°; 60°; 30°; 0°; ⫺30°; ⫺60°.
Fig. 5.
OSNRs for different electrode positions and filters. Stimulation frequency=25 Hz. The figure is organised as the Fig. 4.
358
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
nals reported in Fig. 3 (electrode placement position 2, orientation ⫺30°) was 0.15 at 16.67 Hz and 0.05 at 25 Hz; after the comb filtering, it was respectively 2.7 and 3.0. Considering all the electrode configurations, the OSNR increased on average by a factor of 10 due to filtering. The use of the 20 ms blanking window, in addition to the comb filter, further enhanced OSNR (for the same example reported in Fig. 3 the OSNR obtained with blanking plus comb filter was 4 and 8.9 for 16.67 and 25 Hz respectively). 3.3. Electrodes placement Concerning the effect of the electrodes positioning, placement 2 produced the best OSNRs for most of the different orientations analysed, particularly when the stimulation frequency was 25 Hz. At placement 2 the best result was always exhibited by the electrodes orientation 0°, as to say when the electrodes were aligned longitudinally. The VSNR was 15.85, that was about two times the OSNR. At 16.67 Hz position 3 showed comparably high values of OSNR. Interestingly, the maximum was obtained with an electrodes orientation of 90°, as to say when the recording electrodes were arranged perpendicularly to the line of the stimulating electrodes. In this situation the VSNR was 7.92, as to say about 1.5 times the OSNR (EI=0.66). Also, for the stimulation frequency 25 Hz, for electrodes placement 3 the maximum OSNR was obtained for an orientation perpendicular to the line of the stimulating electrodes even if in this case the OSNR was on average reduced. Placement 5 showed quite contradictory results. Actually the best OSNR for this location was obtained for an orientation of ⫺30° at 16.67 Hz, and for an orientation of 90° at 25 Hz. Placement 6 exhibited similar results at 16.67 Hz, while at 25 Hz the OSNR of the ⫺30° orientation dramatically dropped to zero (blanking plus comb) and to a slightly negative value (comb filter alone). Electrodes placement 1 deserves interest from the practical application point of view. Actually, this electrodes location yields relatively low OSNRs for all the orientations, even if the VSNR were comparable or higher than in other locations (VSNR=8.17 for orientation 0°, VSNR=5.21 for orientation 90°). The Efficiency Index was EI=0.2 for orientation 0° and 0.36 for orientation 90°. Interestingly at 16.67 Hz the maximum OSNR was obtained, similarly with location 3, with an orientation of the recording electrodes of 90°, as to say perpendicular to the line of the stimulation electrodes. In this situation the ratio between OSNR and VSNR was EI=0.62. At 25 Hz the maximum was instead exhibited for an orientation of 30° (EI=0.42). Placement 4 gave the worst results in all conditions. In particular it produced unrealistic negative values at a stimulation frequency of 16.67 Hz.
4. Discussion The considerable enhancement of the Operative Signal to Noise Ratio (OSNR) due to comb filtering is encouraging for the applicability of the homologous stimulation. This means that it is possible to extract the volitional EMG component from the artefacted signal and to use it in a stimulation control scheme. From the practical implementation point of view it is also interesting to observe the further enhancement obtained by the addition of a 20 ms blanking window to the comb filter. This is the result of an increased artefact rejection capability. As the blanking window reduces the number of samples on which the comb filter is applied, the processing time can also take advantage of this procedure. The OSNRs were always higher at 25 Hz stimulation frequency than at 16.67 Hz. The result is consistent with the observation that different stimulation amplitudes are required at different frequencies to obtain a given level of force. In particular the stimulus amplitude was lower at the higher frequencies. Therefore, the non-voluntary contribution to the recorded signal (Ystim, at the denominator in Eqs. (3) and (4)) was smaller at 25 Hz than at 16.67 Hz, and the signal to noise ratios increased. The analysis of the different electrodes locations and orientations revealed some interesting phenomena. Some of the results can be appreciated if we compare the OSNR to the VRSN, as to say the signal to noise ratio in the operative condition to the situation where the voluntary contraction is not superimposed on the electrically induced contraction. It appears, for example, that in both position 1 and 2, where the recording electrodes were just on line with the rectus femoris muscle, the highest VSNR were obtained with an orientation of 0° with respect to the longitudinal line. In operating conditions, when the voluntary contraction was superimposed on to the electrically elicited contraction, the OSNR for placement 2 was still higher at orientation 0°, while for placement 1, the OSNR resulted higher at orientation 90°. This can be explained by considering that with an orientation of 90° with respect to the line of the stimulating electrodes the stimulus artefact has a much higher common mode component and this can be more easily rejected by the differential amplifiers. This effect was extremely evident in position 3, that, for all stimulation frequencies and filtering procedures exhibited the maximum OSNR at orientation 90°. Also the electrodes placement 5 showed the highest OSNR for an orientation of 90° at stimulation frequency 25 Hz. However this result has to be connected to the relative contribution of the artefact component within the compound signal recorded. For example at 25 Hz stimulation frequency, when the stimulation amplitude to obtain a given level of force is lower than with 16.67 Hz, the orientation of 30° yields a better result than the 90° orientation for the electrodes placement 1. For position
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
5 the relative order of magnitude of OSNR was reversed between stimulation frequency 25 and 16.67 Hz. Here, as well as for position 6, the recording electrodes are distally located and it is likely that a relatively low contribution of the rectus femoris activity is detected in relation to the activity of the Vasti muscles (medial and lateral respectively). Similar considerations can be made to explain the surprising finding of a consistently low or negative value of the OSNR for placement 4. The low values could be explained by considering that since the electrodes were relatively marginally located with respect to the muscle, the signals detected for a voluntary contraction were relatively small. The negative values of the OSNR represent a reduction of the compound signal with respect to the stimulation signal recorded alone. This could appear paradoxical at a glance, but can be explained by a swelling of the muscle belly, due to the muscle contraction, and a consequent displacement of the electrodes that in turn can reduce the signal recorded. Following these observations it appears that defining the optimal electrodes location and orientation is not straightforward in all the conditions. However some indications have emerged from our systematic study. It is suggsted however that for any practical application, a careful investigation of the performance and of the pros and cons of each solution is carried out on an individual basis.
Acknowledgements This work has been carried out with the partial support of the Biomed II European projects SENIAM, SENSATIONS, and Neuros2.
References [1] Bigland B, Lippold OCJ. The relation between force, velocity and integrated electrical activity in human muscles. J Physiol 1954;123:214–24. [2] De Luca CJ. The use of surface electromyography in biomechanics. J Appl Biomech 1997;13:135–63. [3] Dolan P, Adams MA. The relationship between EMG activity and extensor moment generation in the erector spinae muscle during bending and lifting activities. J Biomech 1993;26:513–22. [4] Durfee WK, MacLean KE. Methods for estimating isometric curves of electrically stimulated muscle. IEEE Trans BME 1989;36(7):654–67. [5] Graupe D. EMG pattern analysis for patient-responsive control of FES in paraplegics for walker-supported walking. IEEE Trans BME 1989;36(7):711–9. [6] Kadaba MP, Wootten ME, Gainey J, Cochran GVB. Repeatibility of phasic muscle activity: performance of surface and intramuscular wire electrodes in gait analysis. J Orthop Res 1985;3:350–9. [7] McGill KC, Cummings KL, Dorfman LJ, Berlizot BB, Luetkemeyer K, Nishimura DG, Widrow B. On the nature and elimination of stimulus artifact in nerve signals evoked and recorded using surface electrodes. IEEE Trans BME 1982;29(2):129–37.
359
[8] Merletti R, Knaflitz M, De Luca CJ. Myoelectric manifestation of fatigue in voluntary and electrically elicited contractions. J Appl Physiol 1990;69:1810–9. [9] Moritani T, De Vries HE. Re-examination of the relationship between the surface integrated electromyogram (IEMG) and the force of isometric contraction. Am J Phys Med 1978;57(6):263–77. [10] Park E, Meek S. Adaptive filtering of the electromyographic signal for prosthetic control and force estimation. IEEE Trans BME 1995;42:1048–52. [11] Perry J, Hoffer MN. Preoperative and postoperative dynamic electromyography as an aid in planning tendon transfer in children with cerebral palsy. J Bone Joint Surg 1977;59A:531–8. [12] Roy S, De Luca CJ, Schneider J. Effects of electrode location on myoelectric conduction velocity and median frequency estimates. J Appl Physiol 1986;61:1510–7. [13] Saxena S, Nikolic S, Popovic D. An EMG-controlled grasping system for tetraplegics. J Rehab Res Dev 1995;32(1):17–24. [14] Sennels S, Biering-Soresen F, Andersen OT, Hansen SD. Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG signal. IEEE Trans BME 1997;5(2):195–206. [15] Shiavi R, Bugle H, Limbird T. Electromyographic gait assessment. Part 1—adult EMG profiles and walking speed. J Rehab Res Dev 1987;24(2):13–23. [16] Shiavi R, Frigo C, Pedotti A. Elecromyographic signals during gait: criteria for envelope filtering and number of strides. Med Biol Eng Comput 1998;35:171–8. [17] Shiavi R, Green N. Ensemble averaging of locomotor electromyographic patterns using interpolation. Med Biol Eng Comput 1983;21:573–8. [18] Solomonow M, Baratta R, Shoji H, D’Ambrosia RD. The myoelectric signal of electrically stimulated muscle during recruitment: an inherent feedback parameter for a closed loop control scheme. IEEE Trans BME 1986;33(8):735–44. [19] Thorsen R, Ferrarin M, Spadone R, Frigo C. Functional control of the hand in tetraplegics based on residual synergistic EMG activity. Artif Organs 1999;23(5):470–3. [20] Thorsen R. An artefact suppressing fast-recovery myoelectric amplifier. IEEE Trans BME 1999;46:764–6. [21] Winter DA, Yack HJ. EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroenceph Clin Neurophysiol 1987;67:402–11. [22] Yang JF, Winter DA. Electromyographic amplitude normalisation methods: improving their sensitivity as diagnostic tools in gait analysis. Arch Phys Med Rehab 1984;65:517–21. Carlo Albino Frigo was born in Cittiglio (Varese), Italy, on August 3, 1952. He graduated in mechanical engineering (bioengineering–biological control systems) at the Polytechnic of Milan, Italy in 1976. He was with the Department of Electronics, Polytechnic of Milan from 1981 to 1989, then he became part of the newly constituted Department of Bioengineering of the Polytechnic of Milan. He is presently Associated Professor in the Faculty of Engineering at the the Polytechnic of Milan. His scientific research interests include: biomechanics of human movement, modelling, motor control systems, methodologies for movement analysis and clinical applications, prostheses, orthoses, Functional Electrical Stimulation and ergonomics. He is associated with the Bioengineering Centre of the ‘Don Carlos Gnocchi Foundation I.R.C.C.S.—Polytechnic of Milan’, where he has the responsibility of the Laboratory of Gait Analysis and related research. He has been responsible for national and European research projects in the field of motor co-ordination and recovery of motor functions.
360
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
Maurizio Ferrrarin was born in Milan, Italy, in 1964. He received an M.Sc. in electronic engineering and an Ph.D. in bioengineering from the Polytechnic of Milan in 1989 and 1993 respectively. He is a researcher at the Bioengineering Centre (Fnd Don Carlo Gnocchi I.R.C.C.S. ONLUS and Polytechnic of Milan), where he is responsible for the Laboratory for the Study of Motor Recovery (LaRMo). He is also temporary Professor of Rehabilitation Robotics at the Polytechnic of Milan. His main research interests include functional electrical stimulation and innovative orthosis for motor recovery in paralysed persons, clinical gait analysis, biomechanics, spasticity evaluation, ergonomics of wheelchair propulsion and of seat cushions. Currently he is Scientific Responsible of a Ministry of Health research project on neuroprosthesis for the recovery of walking in SCI persons. Dr Ferrarin is a member of IEEE/EMBS, IFESS and SIAMoC (Italian Society of Clinical Movement Analysis) Societies. William Frasson was born in Sondalo, Italy, in 1975. He is currently pursuing a masters degree in biomedical engineering at the Polytechnic of Milan, Italy, with a thesis on the EMG-controlled neuroprosthesis for restoring motor functions of paretic muscles. His major research interests include neuroprostheses, electronic design and digital signal processing.
Esteban Enrique Pavan received an M.Sc. in electronics (biomedical) engineering from the Polytechnic of Milan, Italy, in 1997 (topic: optimisation of a neuroprosthetic device for the training of paraplegics). He is currently a Ph.D. student in bioengineering (topic: advanced control systems for FES-assisted neuroprostheses for paraplegics) and research fellow at the Bioengineering Centre (Fnd Don Carlo Gnocchi—Polytechnic of Milan). His research interests include control systems for paralysed human extremities, investigations of modelling musculoskeletal dynamic behaviour by application of electrical stimulation and biomechanics of human movement. Rune Thorsen, born in Denmark 1967, received his M.Sc.e.e degree from the Technical University of Denmark in 1994 and his Ph.D. in bioengineering in 1997. As a researcher in the Danish company, Asah Medico A/S, he carried out research for the European project EPCES (1994–97), after which he started a three year Post. Doc. position in the European project NEUROS (under the TMR programme). In the first year he worked in Italy at Centro di Bioengegneria, Milano and the second year was in The Netherlands working at Universiteit Twente. He is presently conducting experimental work in collaboration with Salisbury District Hospital and University College London in England. His main research interest is signal processing, electronics, functional electrical stimulation and use of EMG signals for control.
EMG signals detection and processing for on-line control of functional electrical stimulation C. Frigo a
a,*
, M. Ferrarin a, W. Frasson a, E. Pavan a, R. Thorsen
b
Centro di Bioingegneria, Fnd. Don Gnocchi I.R.C.C.S.—Politecnico di Milano, Milan, Italy b Medical Physics and Bioengineering, University College London, London, UK
Abstract The surface EMG signal detected from voluntarily activated muscles can be used as a control signal for functional neuromuscular electrical stimulation. A proper positioning of the recording electrodes in relation to the stimulation electrodes, and a proper processing of the recorded signals is required to reduce the stimulus artefact and the non-voluntary contribution (M-wave). Six orientations and six locations of the recording electrodes were investigated in the present work. A comb filter (with and without a blanking windowing) was applied to remove the signal components synchronously correlated to the stimulus. An operative definition of the signal to noise ratio and an efficiency index were implemented. It resulted that when the recording electrodes were located within the two stimulation electrodes the best orientation was perpendicular to the longitudinal line. However the best absolute indexes were obtained when the recording electrodes were located externally of the stimulation electrodes, and in that case the best orientation was longitudinal. Concerning the filtering procedure, the use of a blanking window before the application of the comb filter, gave the best performance. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Surface EMG; FES control; Electrode placement; EMG signal processing
1. Introduction Surface Electromyogram (EMG) may be aimed at many different objectives. For example the extraction of features related to patterns of movement [6,15– 17,21,22]; the analysis of the force–EMG relationship in particular conditions [1,3,9]; the co-ordination of muscles activity in a particular motor task [3,15,21]. In other cases the objective is to identify signs of muscle degeneration or abnormal behaviour [6,11]. Muscle fatigue and changes in the Motor Unit Action Potential (MUAPT) propagation mechanism can also be analysed through proper modelling and use of statistical indicators [8,12]. In most of these applications the signal recording conditions can be (and must be) controlled quite carefully. Isometric–isotonic conditions are in general required to avoid motion artefacts, to insure that recording is made from a pre-defined portion of the muscle, to guarantee
* Corresponding author. Tel.: +39-02-40308305; fax: +39-024048919. E-mail address: [email protected] (C. Frigo).
signal stationarity, to avoid non linearities connected to changes in muscle geometry and to movements. When muscle co-ordination is the objective of the study, and the surface EMG signal is detected in dynamic conditions, all the above sources of artefact and non linearities are likely to affect the signal. Electrode size and arrangement, signal conditioning, and processing algorithms have to be properly selected and optimised in an attempt to reduce these detrimental effects and to improve the quality of the signal [2,12]. Detection of surface EMG signals in the presence of electrical stimulation presents additional difficulties because the signals delivered by the stimulation electrodes can be much higher than the signals generated by the muscles, and thus can saturate the amplifiers for a considerable time. Many examples can be found in literature [4,5,7,10,18] where different approaches have been used to reject stimulation artefacts. However it’s relatively recent [13,14,19] that these techniques have been applied in a control scheme for Functional Electrical Stimulation (FES). One of the projects our group is working on concerns the use of volitional EMG signals to modulate the stimulation strength delivered to paralysed muscles. A particular application (here named
1050-6411/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 1 0 5 0 - 6 4 1 1 ( 0 0 ) 0 0 0 2 6 - 2
352
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
‘homologous stimulation’) is that of detecting the residual activity of a partially paralysed muscle and using it to control the stimulation of the muscle itself. This is the worst operative condition since the stimulation artefact is the dominant component in the recorded signal. One of the authors of this paper originally designed the first prototype of a portable device (MeCFES— Myoelectrically Controlled Functional Electrical Stimulator) that included one channel of EMG recording, one channel of stimulation, and a Digital Signal Processor (DSP). It was designed for the recovery of hand grasping [19]. A new prototype (MuMeCS—Multichannel Myoelectrically Controlled Stimulator) has been subsequently designed for the application of surface FES to lower limbs, with the purpose of recovering standing up, sitting down, and walking with external support. Some techniques of stimulation artefact reduction have been incorporated in the hardware design, and a special filtering procedure has been implemented in the DSP. Further enhancement of the signal to noise ratio can be obtained through proper positioning of the recording electrodes in relation to the stimulation electrodes. This paper presents the effects of different electrodes arrangements and filtering procedures on the capability of the system to extract the volitional component from the artefacted EMG signal. The aim of the study was to define the criteria for an optimal compromise between artefact rejection and voluntary muscle activity detection.
2. Methods 2.1. The multichannel myoelectrically controlled stimulator (MuMeCS) device description Fig. 1(A) shows the scheme of the MuMeCS portable device. It consisted of two integrated parts: the 4 channels EMG recording unit (differential amplifiers, gain 5000, input impedance 10 G⍀) and the 4 channels stimulation unit (bi-phasic wave shapes with neutral interpulse interval, total duration in the range 20–900 µs, 5– 100 Hz frequency range, 0–100 mA current range). Four analogic signals (0–3 V) could also be collected from additional sensor (foot switches, electrogoniometres) and used in the stimulation control strategy. All the input signals were sampled at 1 kHz, converted into a digital form by an analog to digital (A/D) 12 bits converter, and sent to the Digital Signal Processor (DSP). A digital to analog (D/A) 10 bits converter provided the signal to the stimulation unit which in turn controlled the current delivered to the isolation unit (electric transformer). Due to this control scheme the current delivered to the subject was load independent. The electric transformer also guarantees that no net electrical charge was injected into the patient.
The stimulation artefacts reduction relied on the following features: (a) fast recovery from input overloads, presented by the instrumentation amplifiers [20] (b) automatic compensation of DC-offset fluctuations by a non-linear feedback loop [20] (c) low stimulator output impedance, by means of the electric transformer, at a value that is one dimension order less than patient’s skin impedance When the recording electrodes are relatively close to the stimulation electrodes the above features are not sufficient for a complete rejection of the stimulus artefact. Fig. 2 shows some examples of signals recorded from different muscles in the absence of voluntary contraction and during electrical stimulation of the rectus femoris muscle. The stimulation pattern consisted of a positive and a negative pulse of 0.3 ms duration each, separated by a neutral inter-pulse interval of 0.3 ms. Pulse amplitude was 40 mA and stimulation frequency was 16.67 Hz. The surface stimulation electrodes (5×9 cm) were aligned longitudinally to the muscle at an inter-electrode distance of 16 cm. The recording surface electrodes (0.8 cm ⭋) were positioned on the muscle bellies, slightly displaced towards the tendon extremity, 3 cm apart, and aligned with the muscle fibres. In this experiment the ground electrode was positioned, according to usual practice, on the wrist. A high pass filter (10 Hz, to avoid base line oscillations) and the anti-aliasing low-pass active filter shown in Fig. 1 (500 Hz, Bessel, 2nd order) were applied on the EMG signals before data sampling. It clearly appears that the stimulus artefact is strongly detected by the electrodes closer to the stimulation site. In addition, the muscle response to the stimulus (Mwave) is present on the stimulated muscle and on the synergistic ones (vastus lateralis). 2.2. The experimental conditions The effect of different orientations and locations of the recording electrodes on the signal recorded in the presence of homologous electrical stimulation was investigated systematically on a healthy subject. The electrical stimulation was delivered onto the rectus femoris muscle of the left leg, with stimulation electrodes positioned as described above. The subject seated on a bench with the back supported by a backrest at 60°. The hip was thus flexed by 60°. The ankle of the testing leg was fastened to a rigid structure that maintained the knee flexed at about 45° and allowed measuring the force (through a strain gauge cell) generated at the ankle by the knee extensor muscles. The tests were conducted as follows (see Fig. 3): 1. The subject was asked to perform a Maximum Volun-
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
353
Fig. 1. (A) Schematic representation of the MuMeCS device. The Digital Signal Processor (DSP) receives the differentially amplified and prefiltered signals from the analog to digital (A/D) converter, processes the data on line and sends a stimulation control signal to the digital to analog (D/A) converter. A personal computer (PC) can interact with the DSP during the testing and set up phases. The stimulation unit is galvanically isolated from the patient through a low impedance transformer. (B) Recording electrodes arrangement on a hexagonal plate. (C) Schematic view of the considered placements of the recording electrodes in relation to the stimulation electrodes on the left thigh.
tary Contraction (MVC) for about 4 s, and then, under visual feedback of the force produced at the ankle, to reduce the contraction to 40% MVC for 4 s and then to relax. 2. An electrical stimulation producing 40% MVC was then applied for 6 s. After 2 s from the stimulation onset the subject was asked to increase the force at the ankle by an additional 40% MVC through a voluntary activation of the knee extensor muscles. Therefore, in this phase a total of about 80% MVC was achieved. After 4 s the electrical stimulation was switched off while the subject maintained the voluntary contraction for additional 4 s at 40% MVC. Stimulation parameters were the same as in the experiment described previously apart from stimulation intensity that was adjusted to obtain the desired level of muscle contraction. In this experiment two stimulation
frequencies were considered: 16.67 and 25 Hz. These values were chosen because they fall in the range that is usually adopted for neuromuscular stimulation, and are submultiples of the power line frequency (in Europe 50 Hz). This allowed us to reduce the stimulation component and all its multiple frequencies, including the power line frequency, by applying a single comb filter (see below). At 16.67 Hz stimulation frequency the 40% of MVC was obtained with a current of about 60 mA, while at 25 Hz only 50 mA were required. The recording electrodes were arranged in three bipolar couples and mounted on a hexagonal plate. The relative orientation was spaced by 60° [Fig. 1(B)]. This allowed us to test the following orientations with respect to the stimulation electrodes: 90°, +30°, ⫺30°. The distance between the two electrodes in each couple was 5 cm to allow the ground reference electrode to be located in the centre of the plate.
354
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
Fig. 2. Signals recorded on different muscle locations during electrical stimulation of rectus femoris (I=40 mA, f=16.67 Hz). In (A) the signal was recorded on the triceps brachialis muscle, in (B) on the erector spinae; in (C) on the gluteus medius; in (D) on the medial hamstrings; in (E) on the vastus lateralis, and in (F) it was on the same stimulated muscle (rectus femoris). The amplitude scale is in mV. Note that the graphs on the left side are 10× magnified with respect to those on the right. Signals were only pre-processed by the adaptive gain amplifiers and Bessel antialiasing filter.
The electrodes mounting plate was placed in the following locations [see Fig. 1(C)]: 1. in the middle between the two stimulation electrodes 2. proximal to the stimulation electrodes (still in line with them) 3. in between the two stimulation electrodes but 4 cm medially 4. as in (3) but 4 cm laterally 5. distal–medial to the stimulation electrodes
6. distal–lateral to the stimulation electrodes For location 1 and 2 the hexagonal plate was also rotated by 30°, in order to analyse the following three additional orientations: +60°, 0°, ⫺60°. 2.3. Signal processing and performance indexes The technique adopted for extracting the voluntary EMG component from the raw artefacted signal was based on the use of a ‘comb’ filter.
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
355
Fig. 3. Example of a recording trial on a healthy subject. In (A) the raw EMG signal is reported as detected from the rectus femoris by electrodes located proximally to the stimulation electrodes (placement 2), and rotated ⫺30° in relation to the longitudinal line. Voluntary contraction about 100% and 40% MVC was produced in a first phase (see the force pattern in D). At time 13 s the electrical stimulation started and lasted 6 s (heavy horizontal bar in section AD). The force produced was approximately 40% MVC. At time 15 s a voluntary activation was elicited and this produced an additional force of 40% MVC. At time 19 s the electrical stimulation was switched off, while the subject exerted the same voluntary force as before (40% MVC). At time 24 s the voluntary force was also terminated. In (B) the signal extracted by the comb filter applied to the EMG signal of (A) is reported; in (C) the RMS of signal (B) for each inter-stimulus interval is shown.
This filter is a finite impulse response (FIR) filter having the following expression: y(n)⫽
x(n)−x(n−NTstm)
冑2
(1)
where: x(n) NTstim y(n) √2
is the raw EMG signal at sampling time n is the inter-stimulus time expressed in number of samples is the filtered EMG signal is a scale factor required to keep the same power in the signal before and after filtering
Two cases were tested and compared: (a) the above comb filter was applied to the whole recorded signal (b) a blanking window of 20 ms (during which the signal was zeroed) was applied after each stimulation pulse, and then the comb filter was applied to the residual time window. The blanking window was aimed at directly rejecting the portion of the signal where the stimulus artefact and the M-wave were likely to occur. The amplitude of the processed signal was quantified through its RMS value. The latter was computed for every stimulus (i), by the following expression:
356
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
冪冘
RMS(i)⫽
1 N
N
yi(n)2
(2)
1
where yi(n) N
is the nth sample of the filtered EMG signal within each post-stimulus interval (not considering the blanking period) is the number of samples considered within the post-stimulus interval.
Fig. 3(C) shows the time course of the RMS value in correspondence with the different contraction phases. In the stationary part of each phase the average RMS value was computed. Let’s call: Yvol Ystim Ystim+vol
the average RMS value in the period (T3) when a 40% MVC was produced voluntarily the average RMS in the period (T1) when the same force level was obtained by the electrical stimulation alone the average RMS in the period (T2) when a 40% of MVC was superimposed to an electrically induced contraction at a force level of 40% MVC.
Theoretically, Ystim should be zero since no voluntary component is present in T1, but, as matter of fact, residual noise and not completely removed artefacts can still be present. Provided that the same stimulation strength is delivered, we can assume that Ystim holds the same value even in the presence of voluntary activity. Therefore, the contribution to Ystim+vol due to the voluntary contraction can be estimated as the difference between Ystim+vol and Ystim. According to our objective, for a given level of voluntary contraction superimposed to the electrical stimulation, an optimal combination of electrodes arrangement and filtering procedure should be able to detect all the voluntary EMG component and to reject all the components associated to the stimulus artefact. We defined an Operative Signal to Noise Ratio (OSNR) with the purpose of quantifying this capability: OSNR⫽
Ystim+vol−Ystim Ystim
Yvol Ystim
OSNR Ystim+vol−Ystim ⫽ EI⫽ VSNR Yvol
(5)
This index represents the capability of a given recording and processing condition to extract the voluntary command from the EMG signal in presence of stimulation, relatively to the same condition without stimulation. EI normally ranges from 0 (no voluntary signal detection), to 1 (no degradation due to stimulation). The results will focus mainly on OSNR, since it allows to compare different configurations and to find which of them one optimises the detection of a voluntary signal in the presence of electrical stimulation.
3. Results Figs. 4 and 5 report the Operative Signal to Noise Ratios (OSNR) obtained with different electrodes placements (1–6, see Fig. 1), electrodes orientations in relation to the stimulating electrodes (90° to ⫺60°), filtering procedures (comb filter alone and a 20 ms blanking in addition to the comb filter), and stimulation frequencies (16.67 and 25 Hz). The effects of all these different conditions will be analysed in the following. 3.1. Stimulation frequency For almost all the recording and processing conditions the highest values of the OSNR were obtained at a stimulation frequency of 25 Hz. In some cases the relative amplitude of the OSNR among the different electrodes placements changed depending on the stimulation frequency. For example the highest value was obtained for electrode placement 2, orientation 0° at 25 Hz. The same electrodes location and orientation yield the lower OSNR at a stimulation frequency of 16.67 Hz. Also, the electrode placement 1 proved to be sensitive to the different stimulation frequencies, as the relative amplitude obtained for the different electrodes orientations changed.
(3) 3.2. Filtering procedure
A Virtual Signal to Noise Ratio can instead be defined by considering the voluntary EMG signal measured in phase T3, when stimulation is not present: VSNR⫽
lower than VSNR. To quantify the degradation of the performance when stimulation is active, the following Efficiency Index (EI) was therefore defined:
(4)
Because of the interaction between voluntary and stimulation components, it is expected that OSNR is in general
The comb filter proved to be considerably efficient in reducing the stimulus artefact and enhancing the voluntary component contribution. In fact the OSNR computed after filtering was in general more than one order of magnitude higher than the OSNR computed on the raw signals. In some cases the improvement was dramatic. For example, the OSNR computed on the raw sig-
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
357
Fig. 4. OSNRs for different recording electrode arrangements and filtering procedures. Stimulation on rectus femoris with a stimulation frequency of 16.67 Hz. Above the stimulus artefact were rejected by the comb filter; below the additional 20 ms blanking window was applied prior to the application of the comb filter. Each set of vertical bars correspond to different placements of the electrodes [the numbering is according to Fig. 1(C)]: (1) centered; (2) proximal, (3) centered medial; (4) centered lateral; (5) distal medial; (6) distal lateral. Each vertical bar corresponds to a different orientation of the recording electrodes. From left to right, in progression: 90°; 60°; 30°; 0°; ⫺30°; ⫺60°.
Fig. 5.
OSNRs for different electrode positions and filters. Stimulation frequency=25 Hz. The figure is organised as the Fig. 4.
358
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
nals reported in Fig. 3 (electrode placement position 2, orientation ⫺30°) was 0.15 at 16.67 Hz and 0.05 at 25 Hz; after the comb filtering, it was respectively 2.7 and 3.0. Considering all the electrode configurations, the OSNR increased on average by a factor of 10 due to filtering. The use of the 20 ms blanking window, in addition to the comb filter, further enhanced OSNR (for the same example reported in Fig. 3 the OSNR obtained with blanking plus comb filter was 4 and 8.9 for 16.67 and 25 Hz respectively). 3.3. Electrodes placement Concerning the effect of the electrodes positioning, placement 2 produced the best OSNRs for most of the different orientations analysed, particularly when the stimulation frequency was 25 Hz. At placement 2 the best result was always exhibited by the electrodes orientation 0°, as to say when the electrodes were aligned longitudinally. The VSNR was 15.85, that was about two times the OSNR. At 16.67 Hz position 3 showed comparably high values of OSNR. Interestingly, the maximum was obtained with an electrodes orientation of 90°, as to say when the recording electrodes were arranged perpendicularly to the line of the stimulating electrodes. In this situation the VSNR was 7.92, as to say about 1.5 times the OSNR (EI=0.66). Also, for the stimulation frequency 25 Hz, for electrodes placement 3 the maximum OSNR was obtained for an orientation perpendicular to the line of the stimulating electrodes even if in this case the OSNR was on average reduced. Placement 5 showed quite contradictory results. Actually the best OSNR for this location was obtained for an orientation of ⫺30° at 16.67 Hz, and for an orientation of 90° at 25 Hz. Placement 6 exhibited similar results at 16.67 Hz, while at 25 Hz the OSNR of the ⫺30° orientation dramatically dropped to zero (blanking plus comb) and to a slightly negative value (comb filter alone). Electrodes placement 1 deserves interest from the practical application point of view. Actually, this electrodes location yields relatively low OSNRs for all the orientations, even if the VSNR were comparable or higher than in other locations (VSNR=8.17 for orientation 0°, VSNR=5.21 for orientation 90°). The Efficiency Index was EI=0.2 for orientation 0° and 0.36 for orientation 90°. Interestingly at 16.67 Hz the maximum OSNR was obtained, similarly with location 3, with an orientation of the recording electrodes of 90°, as to say perpendicular to the line of the stimulation electrodes. In this situation the ratio between OSNR and VSNR was EI=0.62. At 25 Hz the maximum was instead exhibited for an orientation of 30° (EI=0.42). Placement 4 gave the worst results in all conditions. In particular it produced unrealistic negative values at a stimulation frequency of 16.67 Hz.
4. Discussion The considerable enhancement of the Operative Signal to Noise Ratio (OSNR) due to comb filtering is encouraging for the applicability of the homologous stimulation. This means that it is possible to extract the volitional EMG component from the artefacted signal and to use it in a stimulation control scheme. From the practical implementation point of view it is also interesting to observe the further enhancement obtained by the addition of a 20 ms blanking window to the comb filter. This is the result of an increased artefact rejection capability. As the blanking window reduces the number of samples on which the comb filter is applied, the processing time can also take advantage of this procedure. The OSNRs were always higher at 25 Hz stimulation frequency than at 16.67 Hz. The result is consistent with the observation that different stimulation amplitudes are required at different frequencies to obtain a given level of force. In particular the stimulus amplitude was lower at the higher frequencies. Therefore, the non-voluntary contribution to the recorded signal (Ystim, at the denominator in Eqs. (3) and (4)) was smaller at 25 Hz than at 16.67 Hz, and the signal to noise ratios increased. The analysis of the different electrodes locations and orientations revealed some interesting phenomena. Some of the results can be appreciated if we compare the OSNR to the VRSN, as to say the signal to noise ratio in the operative condition to the situation where the voluntary contraction is not superimposed on the electrically induced contraction. It appears, for example, that in both position 1 and 2, where the recording electrodes were just on line with the rectus femoris muscle, the highest VSNR were obtained with an orientation of 0° with respect to the longitudinal line. In operating conditions, when the voluntary contraction was superimposed on to the electrically elicited contraction, the OSNR for placement 2 was still higher at orientation 0°, while for placement 1, the OSNR resulted higher at orientation 90°. This can be explained by considering that with an orientation of 90° with respect to the line of the stimulating electrodes the stimulus artefact has a much higher common mode component and this can be more easily rejected by the differential amplifiers. This effect was extremely evident in position 3, that, for all stimulation frequencies and filtering procedures exhibited the maximum OSNR at orientation 90°. Also the electrodes placement 5 showed the highest OSNR for an orientation of 90° at stimulation frequency 25 Hz. However this result has to be connected to the relative contribution of the artefact component within the compound signal recorded. For example at 25 Hz stimulation frequency, when the stimulation amplitude to obtain a given level of force is lower than with 16.67 Hz, the orientation of 30° yields a better result than the 90° orientation for the electrodes placement 1. For position
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
5 the relative order of magnitude of OSNR was reversed between stimulation frequency 25 and 16.67 Hz. Here, as well as for position 6, the recording electrodes are distally located and it is likely that a relatively low contribution of the rectus femoris activity is detected in relation to the activity of the Vasti muscles (medial and lateral respectively). Similar considerations can be made to explain the surprising finding of a consistently low or negative value of the OSNR for placement 4. The low values could be explained by considering that since the electrodes were relatively marginally located with respect to the muscle, the signals detected for a voluntary contraction were relatively small. The negative values of the OSNR represent a reduction of the compound signal with respect to the stimulation signal recorded alone. This could appear paradoxical at a glance, but can be explained by a swelling of the muscle belly, due to the muscle contraction, and a consequent displacement of the electrodes that in turn can reduce the signal recorded. Following these observations it appears that defining the optimal electrodes location and orientation is not straightforward in all the conditions. However some indications have emerged from our systematic study. It is suggsted however that for any practical application, a careful investigation of the performance and of the pros and cons of each solution is carried out on an individual basis.
Acknowledgements This work has been carried out with the partial support of the Biomed II European projects SENIAM, SENSATIONS, and Neuros2.
References [1] Bigland B, Lippold OCJ. The relation between force, velocity and integrated electrical activity in human muscles. J Physiol 1954;123:214–24. [2] De Luca CJ. The use of surface electromyography in biomechanics. J Appl Biomech 1997;13:135–63. [3] Dolan P, Adams MA. The relationship between EMG activity and extensor moment generation in the erector spinae muscle during bending and lifting activities. J Biomech 1993;26:513–22. [4] Durfee WK, MacLean KE. Methods for estimating isometric curves of electrically stimulated muscle. IEEE Trans BME 1989;36(7):654–67. [5] Graupe D. EMG pattern analysis for patient-responsive control of FES in paraplegics for walker-supported walking. IEEE Trans BME 1989;36(7):711–9. [6] Kadaba MP, Wootten ME, Gainey J, Cochran GVB. Repeatibility of phasic muscle activity: performance of surface and intramuscular wire electrodes in gait analysis. J Orthop Res 1985;3:350–9. [7] McGill KC, Cummings KL, Dorfman LJ, Berlizot BB, Luetkemeyer K, Nishimura DG, Widrow B. On the nature and elimination of stimulus artifact in nerve signals evoked and recorded using surface electrodes. IEEE Trans BME 1982;29(2):129–37.
359
[8] Merletti R, Knaflitz M, De Luca CJ. Myoelectric manifestation of fatigue in voluntary and electrically elicited contractions. J Appl Physiol 1990;69:1810–9. [9] Moritani T, De Vries HE. Re-examination of the relationship between the surface integrated electromyogram (IEMG) and the force of isometric contraction. Am J Phys Med 1978;57(6):263–77. [10] Park E, Meek S. Adaptive filtering of the electromyographic signal for prosthetic control and force estimation. IEEE Trans BME 1995;42:1048–52. [11] Perry J, Hoffer MN. Preoperative and postoperative dynamic electromyography as an aid in planning tendon transfer in children with cerebral palsy. J Bone Joint Surg 1977;59A:531–8. [12] Roy S, De Luca CJ, Schneider J. Effects of electrode location on myoelectric conduction velocity and median frequency estimates. J Appl Physiol 1986;61:1510–7. [13] Saxena S, Nikolic S, Popovic D. An EMG-controlled grasping system for tetraplegics. J Rehab Res Dev 1995;32(1):17–24. [14] Sennels S, Biering-Soresen F, Andersen OT, Hansen SD. Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG signal. IEEE Trans BME 1997;5(2):195–206. [15] Shiavi R, Bugle H, Limbird T. Electromyographic gait assessment. Part 1—adult EMG profiles and walking speed. J Rehab Res Dev 1987;24(2):13–23. [16] Shiavi R, Frigo C, Pedotti A. Elecromyographic signals during gait: criteria for envelope filtering and number of strides. Med Biol Eng Comput 1998;35:171–8. [17] Shiavi R, Green N. Ensemble averaging of locomotor electromyographic patterns using interpolation. Med Biol Eng Comput 1983;21:573–8. [18] Solomonow M, Baratta R, Shoji H, D’Ambrosia RD. The myoelectric signal of electrically stimulated muscle during recruitment: an inherent feedback parameter for a closed loop control scheme. IEEE Trans BME 1986;33(8):735–44. [19] Thorsen R, Ferrarin M, Spadone R, Frigo C. Functional control of the hand in tetraplegics based on residual synergistic EMG activity. Artif Organs 1999;23(5):470–3. [20] Thorsen R. An artefact suppressing fast-recovery myoelectric amplifier. IEEE Trans BME 1999;46:764–6. [21] Winter DA, Yack HJ. EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroenceph Clin Neurophysiol 1987;67:402–11. [22] Yang JF, Winter DA. Electromyographic amplitude normalisation methods: improving their sensitivity as diagnostic tools in gait analysis. Arch Phys Med Rehab 1984;65:517–21. Carlo Albino Frigo was born in Cittiglio (Varese), Italy, on August 3, 1952. He graduated in mechanical engineering (bioengineering–biological control systems) at the Polytechnic of Milan, Italy in 1976. He was with the Department of Electronics, Polytechnic of Milan from 1981 to 1989, then he became part of the newly constituted Department of Bioengineering of the Polytechnic of Milan. He is presently Associated Professor in the Faculty of Engineering at the the Polytechnic of Milan. His scientific research interests include: biomechanics of human movement, modelling, motor control systems, methodologies for movement analysis and clinical applications, prostheses, orthoses, Functional Electrical Stimulation and ergonomics. He is associated with the Bioengineering Centre of the ‘Don Carlos Gnocchi Foundation I.R.C.C.S.—Polytechnic of Milan’, where he has the responsibility of the Laboratory of Gait Analysis and related research. He has been responsible for national and European research projects in the field of motor co-ordination and recovery of motor functions.
360
C. Frigo et al. / Journal of Electromyography and Kinesiology 10 (2000) 351–360
Maurizio Ferrrarin was born in Milan, Italy, in 1964. He received an M.Sc. in electronic engineering and an Ph.D. in bioengineering from the Polytechnic of Milan in 1989 and 1993 respectively. He is a researcher at the Bioengineering Centre (Fnd Don Carlo Gnocchi I.R.C.C.S. ONLUS and Polytechnic of Milan), where he is responsible for the Laboratory for the Study of Motor Recovery (LaRMo). He is also temporary Professor of Rehabilitation Robotics at the Polytechnic of Milan. His main research interests include functional electrical stimulation and innovative orthosis for motor recovery in paralysed persons, clinical gait analysis, biomechanics, spasticity evaluation, ergonomics of wheelchair propulsion and of seat cushions. Currently he is Scientific Responsible of a Ministry of Health research project on neuroprosthesis for the recovery of walking in SCI persons. Dr Ferrarin is a member of IEEE/EMBS, IFESS and SIAMoC (Italian Society of Clinical Movement Analysis) Societies. William Frasson was born in Sondalo, Italy, in 1975. He is currently pursuing a masters degree in biomedical engineering at the Polytechnic of Milan, Italy, with a thesis on the EMG-controlled neuroprosthesis for restoring motor functions of paretic muscles. His major research interests include neuroprostheses, electronic design and digital signal processing.
Esteban Enrique Pavan received an M.Sc. in electronics (biomedical) engineering from the Polytechnic of Milan, Italy, in 1997 (topic: optimisation of a neuroprosthetic device for the training of paraplegics). He is currently a Ph.D. student in bioengineering (topic: advanced control systems for FES-assisted neuroprostheses for paraplegics) and research fellow at the Bioengineering Centre (Fnd Don Carlo Gnocchi—Polytechnic of Milan). His research interests include control systems for paralysed human extremities, investigations of modelling musculoskeletal dynamic behaviour by application of electrical stimulation and biomechanics of human movement. Rune Thorsen, born in Denmark 1967, received his M.Sc.e.e degree from the Technical University of Denmark in 1994 and his Ph.D. in bioengineering in 1997. As a researcher in the Danish company, Asah Medico A/S, he carried out research for the European project EPCES (1994–97), after which he started a three year Post. Doc. position in the European project NEUROS (under the TMR programme). In the first year he worked in Italy at Centro di Bioengegneria, Milano and the second year was in The Netherlands working at Universiteit Twente. He is presently conducting experimental work in collaboration with Salisbury District Hospital and University College London in England. His main research interest is signal processing, electronics, functional electrical stimulation and use of EMG signals for control.