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Setup of a Wireless Surface-EMG SystemBernhard PENZLIN1, Alexander KUBE1, Janosch KUNCZIK1

1Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH AachenUniversity, Pauwelsstr. 20, 52074 Aachen, Germany

penzlin@hia.rwth-aachen.de

Abstract. To derive the intention of motion the elec-trical signals can be used, which actuate the muscles.The measuring procedure is known as electromyography(EMG). An overview of available EMG systems and ap-plications for rehabilitation robotics will be given. Thesetup for a system is presented, which measures twomuscles by surface-EMG with 100 samples/s. For trans-mission of the data, a wireless Bluetooth HM-10 moduleand Texas Instruments MSP 430 are used. Further theanalog preprocessing and a primary software to displaythe measured data is described.

KeywordsSurface-EMG, Bluetooth (HM-10), instrumenta-tion device, rehabilitation robotics

1. IntroductionThe healthy human body includes 656 muscles.

For the skeletal muscles two muscles are a pair of an-tagonists. This configuration is necessary to realize theforward and return movement. The ten muscles of thethigh are shown in Fig. 1, lettered in black color. [12]Each skeletal muscle consists of a number of fascicles,each fascicle of many muscle fibers. Each fiber is onemuscle cell.

Fig. 1.The thigh muscles of the right leg and possibleassembly of EMG-electrodes.

The motoric innervation of all muscles is an electri-cal process. The brain produces an electrical signal toshorten a muscle, this signal is conducted by axons. Theresting potential of the inner part of an axon is -70mV.When an electrical signal reaches a cell, ion pumps startto pass sodium ions into the inner part and potassiumions out. Thus the potential is changed to ca. 30mV,the action potential. By this way the signal propagatesthrough the axons from the brain to the muscles. Neu-romuscular junctions transfer the signal from axons tomuscular cells. These junctions are synapses, which usethe neurotransmitter acetylcholine. One motor neuroninnervates many neuromuscular junctions. All musclefibers, which are controlled by one motor neuron, are amotor unit (MU). For a more precise movement moreMU are needed (e.g. up to 2000MU for the muscles ofan eye). If a spike of electrical potential reaches a mus-cular fiber, calcium ions are released, and the musclefiber contracts. [9]

The record of this signal is called electromyography(EMG). To measure this electrical signal, thin metalneedles can be sticked to the muscles. So the actionpotential of a single muscle fiber can be observed. Theskin and tissue is conductive material, so a myoelectricsignal can be measured with surface electrodes as well.Electrodes can only detect the compound potential ofa whole muscle or group of muscles. A possible assem-bly of surface-EMG electrodes on the thigh is shown inFig. 1. The blue electrode no. 6 is in a suitable refer-ence position, which must be distant from any muscle.All other electrodes represent the pairs of measurementpositions for the different muscles.

2. State of the artThere are different purposes of measuring EMG-

signals. On the one hand it can be used for the diagno-sis of nervous and muscle diseases, on the other hand itcan be used to derive a locomotor intention. [10] Thisgauged intention can be used to control something withno reaction forces. This seems promising for a generichuman-machine interface and for controlling rehabilita-tion robotics.

2 B. PENZLIN, A. KUBE, J. KUNCZIK, SETUP OF A WIRELESS SURFACE-EMG SYSTEM

2.1. EMG for gesture control and diagnosticsystemsAn affordable EMG-system is the Myo from

Thalmic Labs. By direct distribution from Canada thisarmband costs 199 $. Eight EMG sensors and a 9-DOFinertial measurement unit are integrated in it. The de-vice communicates via Bluetooth. Already some ap-plications are available to control programs using thisdevice. [14]

Noraxon provides a complete wireless diagnosticsystem. One optional part for a biomechanical anal-ysis system is the TELEMYO 2400T G2. This is awireless EMG measurement system including an up to16-channel transmitter. The sampling rate is 1.5 kHz or3 kHz. With this system needle and surface electrodescan be used. [11]

Another wireless EMG-system, Trigno, is providedby Delsys. This system includes a 16 channel EMG and48 accelerometer channels (1 EMG and 3 accelerationchannels per sensor node). The sampling rate is up to2 kHz. Only surface electrodes are available, these aresmall silver coated areas of the bottom size of everybattery-powered sensor node. [4]

Ottobock produces a myoelectric prosthesis. Forthe surface EMG electrodes type 13E200 the bandwidthis 90-450Hz, the sensitivity range is 2.000 to 100.000.The electrodes transmit the data by wire to a control-unit. This system is already available on the market. Inthe presented application, a hand prosthesis, the sam-pling rate is set to 256 samples/s. [1]

2.2. EMG for motion support systemsEMG for control of motion support has been re-

searched for a long time. Already in 2003 a researchteam of the University of Tsukuba, Japan, investigatedon an EMG-based power assist method. The gait cy-cle was reduced to two phases, the EMG signal wasused to determine the level of supply [7]. The methodof control was similar in later experiments of the sameteam. Although the EMG signal becomes more impor-tant for the control of the exoskeleton, it is not onlyused to gain the joint torque, but also to define a statein the gait cycle. [8] In 2004 Prof. Sankai founded Cy-berdyne to develop exoskeletons. The Hybrid AssistiveLimb (HAL) achieved EC-certification in 2013. Cur-rently HAL robot suits are tested in Bochum [3]. Forthe whole development time bioelectrical signals havebeen used for control.

For the control of active orthoses EMG signals areinvestigated as well. A research group of TU Berlin(2008) for instance used a Delsys EMG system to con-trol their prototype. [5] The wearer of the orthosis

needs to have residual neuromuscular function, gener-ated forces are indirectly measured by EMG and ampli-fied by an electrical drive.

The EMG signal can be used to classify a desiredgrasp for prosthetic hands. To classificate gestures themeans of EMG signals and slow sampling rates are suf-ficient. A vibrotactile feedback can provide a feeling ofthe prothestic hand. For gathering the intention of mo-tion for the flexor carpi radialis, for instance,two elec-trodes (Ottobock Company Group: 13E125) are appro-priate. Sampling rates of 100Hz to 200Hz are used.[2]

A research group of KIT, Germany, use the EMGsignals to control the upper limb orthosis. The orthosisis driven by a fluidic actuator and functional electricalstimulation (FES). FES uses electrical currents to in-nervate muscles. For a patient with a neuronal diseasethe FES allows for using the unimpaired muscles. Tocontrol the orthotic limb in case of a paraplegia, EMGsignals from different muscles can be used, such as themusculus frontalis. [13]

3. The assembled systemFor different purposes different sampling and mea-

surement rates are expedient. In case of EMG signalsbeing used for human-machine interfaces the samplingrate can be smaller. To diagnose a disease higher ratesfit better. The specific bandwidth can be implementedin the analog preprocessing steps. The used analog pro-cessing line is shown in Fig 2.

EMG SignalDifference

Amplifier

High-pass

Filter

Precision

Rectifier

Low-pass

Filter

Output

Stage

Fig. 2.Block diagram of the preprocessing of the EMG-signals.

The first stage is realised using an AD 8226 ARMZ.This instrumentation amplifier provides a bandwidth of1.5MHz (G = 1) and an available gain up to G = 1000.The gain of the difference amplifier step is set to ca.200. The following functions are implemented using TITL084 operation amplifiers (OpAmps). The high passis realized by inverting the OpAmp circuit

Ghighpass(jω) = − R · C · jω1 +R · C · jω

= − τ2 · jω1 + τ2 · jω

(1)

where R is an electrical resistance and C the capacity.The time constant τ2 is set to 1.5ms. Thus the cutofffrequency is at 106Hz. This stage reduces direct compo-nents. Rectification is represented by the absolute valuefunction (nonlinear). A low-pass filter acts like an inte-

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grator to hold a voltage level during measurement. Theimplemented behavior is

Glowpass(jω) = − 11 +R · C · jω

= − 11 + τ4 · jω

(2)

where τ4 is set to ca. 80ms, and the cutoff frequency to2Hz. The output stage adapts the signal to the inputrange of the microprocessor. The Texas InstrumentsMSP 430 offers eight 10-bit channels. For ADCs the-oretical sampling rates of up to 2500 kHz are possible.These rates exceed the capabilities of the CPU (16 MHzclock).

To comprehend the purpose of each stage, all pre-processing steps are visualized in Fig. 2. These curvesare taken from a Matlab/SimElectronics model, the in-put signal is a stochastic signal superimposed with adirect component.

Fig. 3.The output of all subsystems shown in Fig. 2.from top to bottom: preamplified EMG signal,high-pass filtered spikes, rectified signal, low-passfiltered signal and adapted output signal.

The whole path of dataflow is visualized in Fig.4. To gather the analog signals a breakout board isdesigned, including all analog preprocessing stages (seeFig. 2). The breakout board is clipped on a Texas

Instruments MSP 430 Launchpad. For analog input thepins P1_5 and P1_7 are used. To setup the Bluetoothtransmission, Bluetooth 4.0 BLE type HM-10 modules(Bluetooth low energy) are used. The sensor stack getsa transmitter breakout board with this module.

A/D

ConverterChannel 2

Channel 1

BluetoothUARTPacketing

Fig. 4.Block diagram of the dataflow.

For receiving the data a second MSP 430 is ex-panded by the same breakout board. The interfacefor the serial transmission from the launchpad to theBluetooth module is UART (Universal AsynchronousReceiver Transmitter) with a Baud rate of 9600. Thepresent values of both channels are transmitted in onepacket. So a sampling rate of 100 samples/s can be re-alized. Each channel gets 10 bits per sample. The serialtransmission is programmed with Energia.

4. Setup of the surface EMG-systemThe configuration of the surface-EMG system is

described in the last section. The sensor stack is pow-ered by 9 V batteries, the Bluetoth receiver by using theUSB (5 V) connection to a computer. The sensor de-vice, covered by a black plastic case, and the receiver,undisguised, are presented in Fig. 5. The uncoveredsensor stack is shown in Fig. 6. The pluggable connec-tion to the adhesive electrodes is implemented by Molex3 connectors.

Fig. 5.Setup of the surface-EMG system with Bluetoothreceiver (on the left) and adhesive electrodes.

4 B. PENZLIN, A. KUBE, J. KUNCZIK, SETUP OF A WIRELESS SURFACE-EMG SYSTEM

Fig. 6.Uncovered sensor stack, with 9 V supply andBluetooth module.

To test the built device, a program to displaythe current EMG-value of both channels was imple-mented, using the programming language Processing(GPL, Casey Reas, Benjamin Fry). Each value is repre-sented by a bar, whose horizontal position correspondsto the current voltage value. The scaling is up to 1024,the values of both channels are shown directly (10 bit).A screenshot of this software during a measurement pro-cedure is displayed in Fig. 7. In this test the electrodeswere sticked on musculus biceps brachii and m. tricepsbrachii. The grey level shows the maximum value ofboth channels (approx. 500).

Fig. 7.Visual display of the test software (Processing),showing current EMG values.

5. DiscussionThe EMG system with its analog components was

successfully built. The wireless transmission with Blue-tooth HM-10 modules is successfully tested. A sam-pling rate of 100 samples/s is realized, so the transmis-sion rate is 250 kB/s. For first tests a essential programvisualizes the current values of both EMG channels.

The properties of the measurement channelsshould be adjusted for further systems . For diagnosticsystems the sampling rate needs to be increased. Theused HM-10 module allows for higher transfer and sam-

pling rates. To recognize gestures with more than twoinvolved muscles, more EMG channels should be imple-mented. The software can be improved with a storagefacility and different display options for captured wave-forms.

For the purpose of rehabilitation robotics it is ques-tionable, how the intention of motion can be measured.For orthosis wearers with slightly decreased muscularfunction mechanical measurements can be applied [6].To increase the accuracy in this case, EMG and me-chanical measurements can be combined. If the muscu-lar function of a patient is strongly decreased, but theresidual neuromuscular function is still extant, an EMGsystem can allow him for using an active orthosis.

AcknowledgementsResearch described in the paper was supervised

by Prof. Dr.-Ing. Dr. med. Steffen Leonhardt andDr.-Ing. Berno Misgeld, Chair for Medical InformationTechnology, Helmholtz-Institute for Biomedical Engi-neering, RWTH Aachen University, Germany.

References[1] CASTELLINI, Claudio, et al., Surface EMG for force con-

trol of mechanical hands.In IEEE International Conferenceon Robotics and Automation, 2008. ICRA 2008. p. 725-730.

[2] CIPRIANI, C., ZACCONE, F., MICERA, S., CARROZZA,M. C. On the Shared Control of an EMG-Controlled Pros-thetic Hand: Analysis of User-Prosthesis Interaction. In IEEETransactions On Robotics. 2008, Vol. 24, No. 1, p. 170 - 184.

[3] CYBERDYNE CARE ROBOTICS GmbH, ZNB - Zentrumfür Neurorobotales Bewegungstraining. accessed: March 13th,2015, http://www.ccr-deutschland.de/.

[4] DELSYS INC. America, TrignoT M Lab. accessed:March 13th, 2015, http://www.delsys.com/products/wireless-emg/trigno-lab/.

[5] FLEISCHER, C., ZIMMERMANN, A. Auswertung von elek-tromyographischen Signalen zur Steuerung von Exoskeletten.In: Informatik - Forschung und Entwicklung. 2008, 22(3) p.173-183.

[6] HIELSCHER, J., MEISS, T., WERTHSCHÜTZKY, R. Novelapproach for estimating muscular activity using mechanicaleffects of the human thigh as an alternative to EMG. Biomed-ical Engineering/Biomedizinische Technik, 2012. 57, no. SI-1Track-R, p. 851-851.

[7] KAWAMOTO, H., et al. Power assist method for HAL-3using EMG-based feedback controller. 2003. IEEE Interna-tional Conference on Systems, Man and Cybernetics. IEEE,2003, vol. 2, p. 1648-1653.

[8] KAWAMOTO, H., et al. Voluntary motion support control ofRobot Suit HAL triggered by bioelectrical signal for hemiple-gia. Engineering in Medicine and Biology Society (EMBC),2010 Annual International Conference of the IEEE. 2010, p.462-466.

[9] KLINKE, R., SILBERNAGL, S. Lehrbuch der Physiologie.2nd ed. Georg Thieme Verlag, Stuttgart, 1996. ISBN: 3-13-796002-9.

[10] LLOYD, D. G. & BESIER, T. F.(2003). An EMG-drivenmusculoskeletal model to estimate muscle forces and knee

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joint moments in vivo. in: Journal of Biomechanics. 2003,vol. 36(6), p. 765-776.

[11] NORAXON INC. America, TeleMyo 2400T G2. ac-cessed: March 13th, 2015, http://www.noraxon.com/products/emg-electromyography/.

[12] PAULSEN, F., WASCHKE, J. Sobotta: Atlas der Anatomiedes Menschen - Tabellen zu Muskeln, Gelenken und Nerven.2nd ed. Elsevier/ Urban & Fischer, München, 2010. ISBN:978-3-437-44074-8.

[13] SCHULZ, S., SCHMITZ, B., WIEGAND, R., PYLATIUK,C., REISCHL, M. The hybrid fluidic driven upper limb or-thosis - ORTHOJACKET. Myoelectric Symposium. 2011

[14] THALMIC LABS INC. Canada, Introducing Myo. accessed:March 13th, 2015, https://www.thalmic.com/en/myo/.

About Authors. . .

Bernhard PENZLIN

was born in Bad Soden am Taunus,Germany, and received the Dipl.-Ing. degree from OvGU University,Magdeburg, Germany, in 2012. Hehad been a scientific employee at thechair for mechatronics, Institute forMobile Systems at OvGU Magde-burg University from end of 2012to September 2014. Currently he isa scientific employee and PhD stu-dent at the Philips Chair for MedicalInformation Technology, Helmholtz-Institute for Biomedical Engineeringat RWTH Aachen University. Hisresearch interests include rehabili-tation robotics, especially exoskele-tons, design of active orthosis andtrajectory control.

Alexander KUBE

was born in Münster, Germany. Af-ter school, he worked as techni-cal assistant at IfaDo (Leibniz Re-search Centre for Working Envi-ronment and Human Factors), TUDortmund. He received B. Sc. de-gree in Electrical Engineering FromRWTH Aachen University, Aachen,Germany in 2014. Currently he is amasters student at RWTH AachenUniversity, Aachen, Germany ma-joring in Systems Engineering andAutomation and works as studentworker at Philips Research, Aachen,Germany.

Janosch KUNCZIK

was born December 15th 1990 inBergisch Gladbach Germany. Af-ter his Abitur in 2010 he workedas a paramedic until he started hisstudies in Electrical engineering, in-formation technology and technicalcomputer science in the undergrad-uate program of the RWTH AachenUniversity in 2011. He finished inSeptember 2014 with the Bachelor’sdegree and studies now in the identi-cally named graduate program ma-joring in System Engineering andAutomation.