Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm
© López et al; licensee BioMed Central Ltd. 2009
Received: 05 August 2008
Accepted: 25 February 2009
Published: 25 February 2009
Myoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these problems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.
Tested in five volunteers, a redundant arrangement was obtained with two pairs of electrodes for each recording channel. The myoelectric signals were electronically amplified, filtered and digitalized, while the processing, fusion algorithms and control were implemented in a personal computer under MATLAB® environment and in a Digital Signal Processor (DSP). The experiments used an industrial robotic manipulator BOSCH SR-800, type SCARA, with four degrees of freedom; however, only the first joint was used to move the end effector to a desired position, the latter obtained as proportional to the EMG amplitude.
Several trials, including disconnecting and reconnecting one electrode and disturbing the signal with synthetic noise, were performed to test the fusion techniques. The results given by VWA and DKF were transformed into joint coordinates and used as command signals to the robotic arm. Even though the resultant signal was not exact, the failure was ignored and the joint reference signal never exceeded the workspace limits.
The fault robustness and safety characteristics of a myoelectric controlled manipulator system were substantially improved. The proposed scheme prevents potential risks for the operator, the equipment and the environment. Both algorithms showed efficient behavior. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators to better assure the system functionality when electrode faults or noisy environment are present.
Teleoperation of robotic devices for rehabilitation and assistive tasks has increased in later years due, in part, to the introduction of simple interfaces with the ability of discerning the operator's intent . Surface electromyography (EMG) represents an efficient signal for control purposes. Furthermore, the operator's movement is not perturbed by the surface electrodes, allowing an easier adaptation to assistive devices.
The high gain amplification required due to the low level of EMG signals makes myoelectric control rather sensitive to amplitude changes. Such variations can lead to difficulties because the controller might receive incompatible values with the robot specifications, i.e., the mechanical articulations may be subjected to displacements or velocities larger than the recommended ranges, which, in turn, lead to the activation of the robot's protection system.
Typical failures in the case of bioelectric potentials are broken electrode connections or sudden changes in the electrode-electrolyte interface due, for example, to the operator's movements or poor contact, which lead to direct input of noise into the control system. Besides, in the situation herein described, some noise is added by the robot power supplies. If all of the above-mentioned potential failures are not properly considered, they may result in damages to the system, the user or even to third parties.
Robots used, say, in service and rehabilitation of the aged or people with disabilities, execute tasks in cooperation with other humans, so that safety considerations are needed. In order to avoid any possible operator's injury, and/or the activation of the robot safety system, various strategies have been proposed in the literature. The method proposed by Kulic  is based on the trajectory planning with the inclusion of the operator's position; in Fukuda et al , instead, entropy appears as good risk indicator of an incorrect or ambiguous command, that is, when the entropy goes beyond a specified threshold level the robot motors stop. In another approach, Fleischer  introduces a soft manipulator with a flexible joint composed of an electro-rheological fluid and a torque controller considering human pain tolerance. To assure safety of an exoskeleton for the knee joint support, in  all sensor data are range-checked and clipped to sensitive boundaries by software, besides other mechanical considerations.
All safety considerations mentioned above prevent human risks by different methods, like stopping the motors or including the operator's position data. The aim of this work is to guarantee the correct and continuous functioning of the system, even in case of failures. For this purpose, two data fusion strategies are proposed, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF) , by means of an arrangement of redundant potentials, that is, combining the EMG signals from two or more acquisition channels in such a way that after the fusion stage, the algorithms provide a more reliable signal to be applied to the control system.
Data fusion techniques are frequently implemented in robotic control, where the information is redundant and/or of diverse nature [7, 8]. When the data sensors are similar, fusion is applied over the signals, but when the data sensors are of different nature, fusion takes place on the control signals . In this application, the interested variables are measured by two or more pairs of electrodes to obtain more information than from a single channel. The sensors (electrodes) differ only in their location and not in their characteristics, and their signals are fused to reduce the sensitivity of the control system relative to electrode failures, so increasing the overall robustness.
The paper is organized as follows: Methods-A presents the system and processing overview. The methodology of acquisition and robot control is discussed in Methods-B and the algorithms for data fusion in Methods-C. In Results, Section A, the two fusion techniques are applied to EMG signals in the presence of failures while a comparative performance under simulated noise conditions is given in Section B. Finally, in the Discussion, the merits and limitations of these algorithms are discussed.
Equipment and processing
Following the recommendations of SENIAM protocol , bipolar EMG's were recorded with a pair of Ag/AgCl electrodes (3M RedDot) placed 20 mm apart. The longitudinal axis of this pair was aligned, when possible (in amputees such positioning may not be realized), along the muscle fibers. An array of two or more identical pairs of electrodes was placed in the volunteer's arm (Fig. 1).
Electronic amplification, optical isolation, and filtering are implemented by a custom-made front-end signal conditioning circuit with the following characteristics:
Amplification stage (AD620, Analog Devices®): Input impedance: 10 MΩ; Gain: 1000; Common Mode Rejection Ratio (CMRR): 120 dB.
Filter: 6th order Band-Pass Filter with cuttoff frequencies 10–500 Hz and Butterworth Coefficients
Sequence of operators applied to EMG signal.
Filtering with a 6th order Butterworth Bandpass filter (10 Hz–500 Hz).
emg filt (k) = filter (emg raw (k))
Normalization with respect to MVC
emg rect (k) = abs (emg norm (k))
Background noise removing
Symmetric Dead Zone
BNT = Background Noise Threshold
emg(k) = DeadZone(emg norm (k)) = if abs (emg norm (k)) ≥ BNT; emg(k) = emg norm (k) otherwise emg(k) = 0
Even when the relationship between EMG amplitude and muscular force is controversial, some features are accepted as estimators in myoelectric control, especially in amputees, due to the impractical measure of the muscle force . Several factors, like electrode location, interelectrode distance, subcutaneous fat layer thickness, make it impossible to consider a generalization of the EMG-force relation in different subjects and experimental sessions . Nevertheless, in the initial setting stage, the system records the maximum voluntary contraction (MVC) and the background noise applying the same procedure described in Table 1. Hence, these values were calculated over the rectified and smoothed signals. Afterwards, with this information, the system executes an adaptive routine for the current environmental conditions and for the specific characteristics of each volunteer. Furthermore, the relation between the muscle force and the command for the robot control was obtained from pairs of agonist-antagonist muscles. This is because the operator was trained based on the functional muscle group, which reduces the influence of each individual muscle.
Regarding the rejection of the noise generated by the power source, the use of notch filters is not recommended in EMG applications because they introduce phase rotation and remove a frequency band, precisely where this signal shows an important power density . The high CMRR of the differential amplifier (120 dB) improves noise rejection.
EMG is presented as a time sequence, which must be mapped to a smaller dimension vector by the computation of several features leading to a muscle force estimator and input to the classifier. A wide spectrum of features can be found in the literature, computed either in the time or frequency domain, or both, as can be seen in  and the references therein cited. Time domain features are widely used due to computational simplicity and real-time control possibilities. For choosing the most adequate, the statistical set proposed by  was evaluated in terms of computational cost and repeatability.
where k = 1,2,... corresponds to the sample time and emg(k) is the myoelectric signal in each sampled time.
Previous informed consent, biceps and triceps EMG signals were recorded during voluntary contractions in 4 normally limbed subjects (3 male, one female, 25 ± 3 years old) and one above elbow amputee (male, 24 years old).
After a period of rest, during which the background noise was recorded, volunteers were instructed to perform a 1s MVC with each muscle to be used in the normalization stage. During the test, the subject was instructed to use this pair of agonist-antagonist muscles to command the manipulator and displace its end effector to the right and left on the workspace. Both statics and dynamics contractions were tested while the type of contraction was chosen by the user.
The decision criterion for planning the trajectory is the sign obtained from the difference between MAV signals of the biceps and the triceps, that is, sign(emgchannel 1(k) - emgchannel 2(k)). The following (arbitrary) criterion was adopted: a biceps contraction causes a rightward displacement of the robotic arm, and the triceps contraction a displacement to the left.
Two algorithms were proposed: Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF). Since the EMG signal recorded during voluntary dynamic contractions can be considered as a band-limited zero-mean Gaussian process, modulated by muscle activity and corrupted by a Gaussian additive white noise , its instantaneous changes of variance provide an indicator of muscle activity as well as the presence of fault-induced noise. For this reason, the variance was chosen as weighting function.
In the first algorithm, a modified average was used, i.e.,VWA(k) = w1(k)emg1(k)+w2(k)emg2(k)
where both coefficients w1(k) and w2(k) satisfy the following conditions,
0 = w1(k),w2(k) = 1
w1(k) + w2(k) = 1
Stochastic estimation tools such as the Kalman filter can be used to combine or fuse information from different media or sensors for hybrid systems. The Decentralized Kalman Filter (DKF) generates the overall signal estimate by minimizing the variances . The DKF can be considered an algebraic equivalent of the Centralized Kalman Filter (CKF). Theoretically, there is no performance loss in the decentralized system, it delivers the same results as the CKF, but the benefits of the DKF are the modular concept that allows to add more sensors to the system, as needed, and an easier parallel implementation .
where i represents the local filter, n is the number of local filters,P i stands for the local variance, (k) is the filtered (estimated) signal, P represents the global variance, and emg est (k) describes the global estimated vector.
Analysis of both algorithms in the presence of failures
Once both algorithms were adjusted, they were experimentally tested under several conditions, as for example, disconnecting and reconnecting one electrode in an alternating manner (a temporary electrode disconnection leads to saturation of the amplification stage). The EMG without perturbations (called noiseless channel) was continuously recorded as reference while the noisy signal was introduced into the robot mathematical model to compute the trajectory with failures. Both graphs were thereafter analyzed for evaluation purposes.
Even when the transformation of this signal to joint coordinates is not exact, the failure is ignored by the control system, and the joint reference signal never exceeded the workspace limits. Under these circumstances, the electrode disconnection can be detected by the operator and therefore corrected.
Performance analysis under different noise conditions
Maximum absolute error (Eq.9) for both algorithms, when the EMG signals were corrupted by Gaussian white noise, and by power line interference.
Gaussian white Noise
50 Hz Interference
max abs error = max(abs(emg(k) - emg est (k))
The procedure described in Section III-A was repeated in all volunteers to compute the average of the absolute error (i.e. infinity norm) for both methods. The maximum absolute error for DKF Fusion was 1.6285 V (STD 0.13) and for VWA was 0.3635 V (STD 0.47), in the group of healthy subjects. The same calculation was made for the amputee; in this case the error for DKF Fusion was 1.978 V and for VWA was 0.4275 V. There are no significant differences, thus the performance is similar in both groups.
The literature does not seem to be abundant in the use of fusion for EMG signals. We could find the report by Silva et al.  applying data fusion of mechanomyography (MMG) signals for prosthesis control. These authors concluded that a multisensor data fusion technique is used as strategy for the generation of binary control signals for an electrically powered prosthesis, none the less, this paper is somewhat unclear in its results and is not well related to the way we use it here.
When the benefits of each algorithm are analyzed, as in any robustness scheme, two aspects must be taken into account: noise sensitivity and computational cost. On one hand, VWA appears as more efficient due to its better sensitivity to noisy signals, but the DKF algorithm is recommended in fusion where the signals come from sensors whose nature is different, like electrodes for electromyography, piezoelectric contact for mechanomyography, accelerometers for acceleromyography (AMG), and condenser microphones for phonomyography (PMG). On the other hand, the computational cost for both algorithms is the same, therefore, this is not a decision factor, and the choice would depend on the expected perturbations and the possibility of incorporating new sensors.
The use of redundant potentials is ultimately limited by the practical possibility of sensing with two or more electrodes on the same muscle group. However, this is not always feasible in amputees because of the shape and space availability on the stump area for attachment of surface electrodes and the problem of adhesion.
The two proposed fusion algorithms, VWA and DKF, have demonstrated an efficient performance. Despite the fact that both algorithms have shown different responses to noise, the manipulator never moved beyond its safety range. Moreover, the true system trajectories followed closely the ideal trajectories generated with the robot mathematical model. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators in order to assure the functionality under electrode faults and noisy environments.
Two data fusion algorithms of EMG signals are proposed in this paper with the aim of improving the fault robustness and safety characteristics of a myoelectric controlled manipulator system. The major advantages for this scheme are: the continuous operation of the manipulator, even in case of electrode disconnection, and the modularity that offers the possibility to include different number and types of sensors. The main contribution of the work proposed here can be centered around two main issues. First, the improvement of the robustness preventing potential risks for the operator and the environment in case of failure, tested under real and simulated noisy conditions. Second, the fact that two simple data fusion algorithms based on the instantaneous variance analysis and without computational cost were applied to EMG.
The two algorithms used demonstrated an acceptable performance.
The authors thank Eugenio Orosco and Julián Ledesma for their cooperation and assistance during the trials. Partially supported by the Universidad Nacional de San Juan (Argentina) and by CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, Grant PIP#6555). Recognition is also given to Pablo Maldonado, MD, traumatologist, for medical advise.
- Krebs H, Dipietro L, Levy-Tzedek S, Fasoli S, Rykman-Berland A, Zipse J, Fawcett J, Stein J, Poizner H, Lo A, Volpe B, Hogan N: A Paradigm Shift for Rehabilitation Robotics. IEEE Engineering in Medicine and Biology Magazine 2008, 4: 61–70. 10.1109/MEMB.2008.919498View ArticleGoogle Scholar
- Kulic D: Safety for Human-Robot Interaction. In Ph.D. dissertation. British Columbia Univ., Vancouver, Canada; 2005.Google Scholar
- Fukuda O, Tsuji T, Kaneko M, Otsuka A: A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions. IEEE Trans Robotics and Automation 2003, 19: 210–222. 10.1109/TRA.2003.808873View ArticleGoogle Scholar
- Fleischer C: Controlling Exoskeletons with EMG signals and a Biomechanical Body Model. In Ph.D. dissertation. Berlin University, Germany; 2007.Google Scholar
- Nakamura T, Saga N, Nauazawa M, Kawamura T: Development of a Soft Manipulator Using a Smart Flexible Joint for Safe Contact with Humans. Proceedings of the 2003 IEEE/ASME lnternational Conference on Advanced Intelligent Mechatronics 2003, 441–446.View ArticleGoogle Scholar
- Brown R, Hwang P: Introduction to Random Signals and Applied Kalman Filtering. John Wiley & Sons; 1997.Google Scholar
- Soria C, Freire E, Carelli R: Stable AGV corridor navigation based on data and control signal fusion. Latin American Applied Research 2006, 36: 71–78.Google Scholar
- Freire E, Bastos Filho T, Sarcinelli Filho M, Carelli R: A New Mobile Robot Control Architecture: Fusion of the Output of Distinct Controllers. IEEE Transactions on Systems Man and Cybernetics 2004,34(Part B-Cybernetics):419–429.View ArticleGoogle Scholar
- SENIAM. "Surface Electromyography for Noninvasive Assessment of Muscle"[http://www.seniam.org]
- Orosco E, López N, Soria C, Guzzo M: Procesamiento de señales mioeléctricas implementado en procesador digital de señales. Proceedings of Iberdiscap 2008 2008, 174–177.Google Scholar
- Zardoshti-Kermani M, Wheeler B, Badie K, Hashemi R: EMG Feature Evaluation for Movement Control of Upper Extremity Prostheses. IEEE Transactions on Rehab Engineering 1995, 3: 324–333. 10.1109/86.481972View ArticleGoogle Scholar
- Merletti R, Parker P: Electromyography: Physiology, Engineering and Noninvasive Applications. IEEE Press – Wiley Interscience. Ed. John Wiley and Sons; 2004.View ArticleGoogle Scholar
- Oskoei M, Hu H: Myoelectric control systems. A survey. Biomedical Signal Processing and Control 2007, 2: 275–294. 10.1016/j.bspc.2007.07.009View ArticleGoogle Scholar
- Hudgins B, Parker P, Scott R: A new Strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 1993, 40: 82–94. 10.1109/10.204774View ArticleGoogle Scholar
- Clancy E, Morin E, Merletti R: Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol 2002, 12: 1–16. 10.1016/S1050-6411(01)00033-5View ArticleGoogle Scholar
- Zecca M, Micera S, Carrozza MC, Dario P: Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal. Critical Reviews in Biomedical Engineering 2002, 30: 459–485. 10.1615/CritRevBiomedEng.v30.i456.80View ArticleGoogle Scholar
- Sciavicco L, Siciliano B: Modelling and Control of Robot Manipulators. In Advanced Textbooks in Control and Signal Processing Series. 2nd edition. Edited by Springer-Verlag, London, UK; 2000.Google Scholar
- Clancy E, Hogan N: Probability Density of the Surface Electromyogram and Its Relation to Amplitude Detectors. IEEE Trans Biomed Eng 1999, 46: 730–739. 10.1109/10.764949View ArticleGoogle Scholar
- Silva J, Chau T, Goldenberg A: MMG-Based Multisensor Data Fusion for Prosthesis Control. Proceedings of the 25 Annual lntemational Conference of the IEEE CMBS 2003, 17–21.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.