Using Forearm Circumference for Automatic Threshold Calibration forSimple EMG Control

James Cannan1 and Huosheng Hu1

Abstract— Training and calibration is normally required forthe users of Electromyographic (EMG) control systems, whichcan require a lot of time and expertise. It is necessary toreduce training and calibration so that EMG control can bedeployed in real-world applications, with minimal hassle. Basedon our previous work, this paper investigates a novel method ofusing an automatic circumference measuring device to determineforearm circumference, in order to estimate maximum voluntarycontraction (MVC) via linear regression. MVC is commonly usedfor threshold adjustment, hence automatic estimation can beused to auto-calibrate EMG thresholds. A new load cell basedautomatic measuring circumference device is demonstrated, andan experiment is performed which confirms the potential of usingforearm circumference for estimating MVC. Experimental resultsconfirm the feasibility of the concept, and the performance of thedeveloped device.

Keywords: Electromyography (EMG), Auto calibration,Anthropometric , Circumference, Maximum Voluntary Con-traction (MVC)

I. INTRODUCTION

Electromyography (EMG) is the study of our muscles elec-trical properties, and has been studied since the mid sixteenthcentury. Fast forward to the 20th century, this discovery ledto technology that is being used in commercial prothesis andanalysis of nerve and muscle function. Recently over the lastdecade, EMG technology has seen a boost in popularity, withnew interfaces being developed, and various applicationsfrom: robotic control, exoskeletons, rehabilitation, to virtualreality and games control. Applications that are developedon forearm EMG are of particular interest, as the musclesthat control the majority of our hand motion are found inthe forearm.

The forearm has a complex muscle configuration, withmuscles overlapping and multi-functional control. If theexact movement of a human hand can be electronicallydeciphered, a natural way of interfacing our biological com-plexity with various forms of technology can be achieved,and provides a rich source of input commands for everydayelectronics and robots. Today there are many instances ofEMG being used in gesture control [16][17][10]. Whichsuggests the possibility that we could one day recreate handfunctionality. The majority of approaches use new signalprocessing and machine learning to increase device accuracy,

*This research has been financially supported by the EPSRC grant -EP/K004638/1. The 1st author is financially supported by EPSRC Stu-dentship EP/P504910/1.

1Both Authors are with the Computer Science and Electronic EngineeringDepartment, University of Essex, Wivenhoe Park, Colchester, CO43SQ,United Kingdom.

but surprisingly little has been done on trying to incorporateanthropometric variables such as forearm structure. To ourknowledge, there have been no implementations that activelyuse forearm anatomy as a compensationary method foradapting EMG systems for multiple users.

EMG signals generated by body movements do containinformation relating to the muscle, and patterns can beextracted with pattern recognition techniques[15]. However,this complex process of signal processing and machinelearning may be simplified by using more direct informationabout the forearm itself. By using forearm circumference, itis possible to roughly estimate Maximum Voluntary Contrac-tion (MVC), as we will demonstrate later in this paper. Thisrelationship could allow the simple calibration of an EMGsystem. In daily operations, training and calibration of EMGsystems may take a few minutes, up to half an hour. If aperson had to train or calibrate an EMG interface every timehe or she wanted to access their device, they would mostlikely abandon the device for a faster and better alternative.This is one of the biggest hurdles that EMG faces before itcan reach the general public.

For simple EMG systems, auto calibration can be achievedby estimating the users MVC. Currently there are no methodsfor automatically estimating MVC before the user beginsmovement, which is why every user is required to calibratea new system. This paper proposes a novel approach toautomatically calibrate EMG thresholds, by estimating MVCfrom forearm circumference. It aims to create a system thatis instantly compatible with any user, with minimal need forcalibration and training. The system takes a small step to-wards making bionic muscle interfaces easier to use. The restof the paper is organized as follows. Section II presents somebackground about Maximum Voluntary Contraction (MVC),and how to validate the potential for estimating MVC, fromforearm circumference. In Section III, the design of a loadcell based automatic circumference armband is described,and a preliminary functional prototype is presented. SectionIV explains the experiment setup and results, along withdemonstrating a better relationship between circumferenceand MVC. Discussion is given in Section V to analyzethe performance of using MVC based on circumference forcalibration. Finally, a brief conclusion and future work ispresented in Section VI.

II. BACKGROUND

There are a number of considerations that need to betaken into account when using EMG, for example electrodeposition, electrode contact, temperature, fatigue, noise and

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body composition. Every person is different, every reading isdifferent, and by beginning to stabilize some of the unknownvariables, and incorporating or considering these values incalculations, it is likely to increase accuracy or at leaststabilize results.

Thresholding techniques, although not necessarily com-plex, do require some calibration between users. This is oftendone by measuring a users Maximum Voluntary Contraction(MVC) by means of amplitude based features, like RootMean Squared (RMS) and Mean Absolute Value (MAV),which can then be used to normalize the EMG signal. MVChelps determine more comfortable activation thresholds forindividual users. A system where one person has to applyintense force to pass a threshold and where others barelyhave to flex their muscles, makes the system unresponsiveto user requirements. Being able to adjust to a proportionof the users maximum strength enables a system to find acomfortable level, making the system more user friendly.

A circumference armband should be able to roughly es-timate MVC, by automatically measuring the circumferenceof the forearm, this is viable as there is a correlation betweenmuscle size and muscle force[3], and muscle force and MVC[11]. From the previous statement we can logical assumemuscle size should be able to predict MVC. Thereforecircumference, which is dependant on muscle size, could beable to predict MVC. However any relationship would likelybe limited to the larger surface muscles, as these muscle areeasily monitored and form the majority of the circumference.

There are circumstances where the larger muscle is notalways the stronger muscle, as there are three importantfactors that contribute towards muscle strength: physiolog-ical strength, neurological strength and mechanical strength.Physiological strength involves factors of muscle size, neuro-logical strength relates to the strength of the signal that tellsyour muscles to contract, and mechanical strength is howefficiently your muscles and bones work together as levers.In addition there are two types of voluntary contractionmuscle fibers, slow twitch and fast twitch. Slow twitchfibers generate little force but operate for long periods oftime, while fast twitch fibers are powerful but tire rapidly.Depending on which muscle fibers you have can influenceyour strength. Level of motivation and visual feedback havealso been shown to influence MVC[7]. Therefore muscle sizemay only be acceptable as an estimator of MVC.

Maximum voluntary contraction is the maximum ability tocontract ones muscles. Logically when related to the MVCof forearm muscles, grip strength becomes a good predictor,as forearm muscles control the majority of hand motions.

Hou et al [9] were able to relate hand grip force with EMGMVC levels, which demonstrate the relationship betweenMVC and hand grip strength. As mentioned previously mus-cle strength is related to muscle size, therefore circumferencehas the potential to predict MVC. However, Hou et al doshow varying ranges of accuracy are produced at differentlevels of MVC, with the most accurate results produced inthe mid MVC range of 30-50%.

Our previous paper[5] suggests the feasibility of estimat-

ing MVC from forearm circumference, however the resultsbetween circumference and MVC were not as correlated asexpected. There was a strong correlation between forearmsize and grip strength, but a much weaker but still positiverelationship between MVC and forearm circumference. Theelectrode positions were placed at a simple to locate positionat the center of the forearm, with the aim of easy andrepeatable estimation of muscle activity. Considering thenumerous studies relating muscle force to MVC lead us tobelieve that the electrodes were not acquiring enough EMGsignal from the main muscles used in gripping, therefore thesetup should be improved, but none the less demonstratedthat upper forearm circumference was capable of roughlyestimating MVC from circumference.

Previously, a preliminary functional prototype of an auto-matic circumference measuring armband was built for test-ing. A full description is provided in [5]. This version of thecircumference armband was a first prototype, it was designedto evaluate the potential of circumference measurability.

A further circumference measuring device has been sub-sequently developed, the aim was to improve the reliabilityof measurements, and greatly reduce the size and amountof moving parts. In response to this, the idea of using asimilar device to an electrical scale, whereby elastic aroundthe forearm would place pressure on the scale, thereforein theory providing a linear relationship between appliedscale pressure and forearm size. Hence the exploration of theAutomatic Circumference Armband V2 (Load Cell based),which is described in the next section.

Attempting to measure forearm size could be used invarious fields of research. It has primary interest within themedical and health industries but could also importantlybe used in area’s of wearable robotics. Detecting humanphysiological characteristics is likely to become a moreimportant field in the future, when an increased numberof wearable devices will be available, making automaticadaption or calibration a very useful feature.

III. DESIGN OF AUTOMATIC CIRCUMFERENCEARMBAND V2 - LOAD CELL BASED

Version 2 of the Automatic Circumference Armband isLoad Cell based. Load cells work on the principle, that achange in resistance is in response to an applied load. Straingauges are essentially resistors which can be bonded to ametallic structure, when the metal is stressed by applyingforce, the resulting strain leads to a change in resistance.Using a circuit similar to a voltage divider or wheat stonebridge, a change in resistance leads to a change in outputvoltage.

A picture of our circumference measurement sensor isshown in figure 1. The load cells are attached to the twometallic beams and are connected to a custom made supportconstructed from Acrylic. The left most spider like pieceof Acrylic is placed on top of the sensor. An elastic strapis placed on the ridges, and each ridge pair allows foradjustment of the sensors sensitivity. Figure 2 of the sensoron a model arm helps visualize the setup.

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Fig. 1: Circumference Armband V2 (Load Cell)

Fig. 2: Circumference Band V2 (Load Cell) on arm

Two load cells have been used, one is for the detection ofthe elastic force applied when wrapped around the forearm,and the other is used as a reference so that any motionartifacts can be removed. The sensor is 29 x 39 x 22mmin size and under 20g.

The readings from the armband are passed into a INA128instrumentation amplifier and amplified to a usable level,where the signal is passed to the 10bit ADC of a 16f877amicrocontroller and transmitted to a computer for processingand graphical representation. In theory this version of thearmband with a 10bit ADC could achieve better than 1mmaccuracy. In practice only around 5-10mm accuracy wasachieved. One benefit of the design is, by simply changingthe size of the elastic strap any body type can be measured.

IV. EXPERIMENTS

A. Experiment Setup

The hardware used for estimating Maximum VolentaryContractions(MVC), included 4 Biometrics differential EMGelectrodes, 1 Dynamometer hand force sensor, and EMGacquiring hardware (as shown in fig 3 ).

For the experiment setup, Four EMG electrodes wereplaced on the forearm at the wrist, mid-forearm and upperforearm, as shown in figure 4, however due to electrode sizeit was found to be impractical to use this setup on the wrist,hence only electrode 1 was used.

During the experiment, participants were asked to holdthe force sensor in neutral position (joystick position), whilehaving the elbow joint at 90 degrees. They were then askedto grip the force sensor as hard as they could for a short

Fig. 3: Biometrics Experiment Hardware, EMG Hardware,Electrodes and Dynamometer (Force Sensor)

Fig. 4: Experiment setup A - On Wrist, B - Mid Forearm, C- Upper Forearm. E1-E4 represent Electrodes 1 to 4. SetupA uses 1 electrode and B and C use 4 Electrodes (Whitedotted line representing electrode 3 on the back).

period of time, with the peak of the Mean Absolute Value(MAV) used to determine the MVC.

B. Experimental Results

A study was performed to assess and confirm the relation-ship of forearm circumference with force and MVC. Wrist,mid-forearm and upper forearm circumference measurementswere taken from 14 subjects aging between 24 and 51.

Reasonable relationships were observed when comparingwrist, mid forearm and upper forearm circumference to forcedata (figure 5), which had average errors of 5.55kg, 5.72kgand 3.61kg respectively. This suggests that if the theoryrelating muscle force to EMG MVC holds, then we should beable to observe a linear relationship between circumferenceand EMG MVC. Compared to previous results in [5], a betterlinear relationship was observed on a couple of the electrodeswhen comparing the upper forearm circumference againstMVC.

The best result of the experiment was observed on Elec-trode 2 on the upper forearm (figure 6), which was placedover the Palmaris longus/Flexor digitorum profundus region,which are involved in wrist and finger flexing. Figure 6demonstrates the relationship between MVC and forearmcircumferences. Although not perfect, it does suggest thepotential of using circumference for estimating MVC. Theupper forearm electrodes produced the most stable results,with E1, E2 and E4 producing the strongest results and E3

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Fig. 5: Grip Force VS Wrist, Mid-Forearm and Upper-Forearm Circumference chart

Fig. 6: Upper Forearm Circumference VS EMG MaximumVoluntary Contraction on Electrode 2. (Solid line: Best Fit,Dotted Line: Average)

producing the weakest in the upper forearm group. The codeUFE1 represents the electrode position, UF equals UpperForearm, E1 equals Electrode 1.

Lines of the average(dotted line) and best fit(solid line)were added to the graphs. This is to show how a systemthat uses the average of the data for threshold calibration,compares to a circumference based technique. Table I showsthe comparison of standard deviation (SD) for the averageline and linear deviation (LD) of the circumference best fitline. Improvement is the percentage improvement betweenlinear and standard deviations.

As you can see from table I, by incorporating circumfer-ence data we are able to reduce the data deviation by up to22.45%. The deviation is lowest on the upper forearm whichhas an average linear deviation of 0.136mV compared to0.219mV of the Mid forearm. The Upper Forearm has betterresults, so will likely be used instead of the mid-forearm. Theelectrode with the lowest deviation came from UFE2 (UpperForearm Electrode 2) with a LD of 0.07957mV using theliner best fit threshold approach, which proved to be 22.45%better than average thresholding. Surprisingly the 2nd lowestdeviation came from UFE1, providing slightly better (0.56%)

Electrode Mean (mV) SD LD Improvment

UFE1 0.226 0.094 0.094 +0.56%UFE2 0.354 0.103 0.08 +22.45%UFE3 0.441 0.116 0.113 + 2.08%UFE4 0.572 0.260 0.259 +0.38%MFE1 0.44 0.232 0.184 +20.91%MFE2 0.333 0.206 0.206 +0.01%MFE3 0.501 0.357 0.357 +0.3%MFE4 0.383 0.13 0.13 +0.22%WE1 0.625 0.342 0.342 +0.03%

TABLE I: Summary of Results table. SD - Standard Devi-ation, LD - Linear Deviation, %Improvement - LD vs SDimprovement

LD results than SD results. However this may improve witha bigger sample size.

As mentioned earlier, and as you can see from table Iresults, estimating MVC by Forearm Circumference maynot work for everyone, but could potentially work for themajority, in some situations it may be necessary to filter datato provide higher accuracy for the overwhelming majority,which for this sample was only performed on UFE2 data,where an outlier outside of two linear deviations was de-tected. This does not mean that the user cannot use the EMGdevice, but instead will be required to do manual calibration.Ideally further anthropometric variables will be needed tohelp compensate for every body composition.

In figure 6, it is possible to see that the average ( dottedline ) of the data provides a reasonable result for adjustingthresholds for a portion of the test group, but completely failsfor individuals at the bottom of the circumference spectrum.The inclusion of circumference data with linear regressionanalysis ( solid red line ) provides a much better result forthe bottom spectrum, while also improving the top range.With a more even spread of circumference data, you wouldbe likely to see an even greater improvement in the top range.

To try and determine how the calculated regression equa-tion would be with new data, 3 additional test participantswere found. The resulting percentage prediction error was11.67% for participant A, 49.03% be B, and 14.72% for C.Suggesting that the regression equation provides an accept-able error for participants A and C, but B would requiremanual calibration.

Some early analysis of the results showed that the datawith the greatest variance was linked to Body Mass Index(BMI), with higher than expected MVC results having a lowBMI, and lower than expected results having a high BMI,this is shown in figure 7. Baars et al[2] state, the further awaythe monitoring electrode is from the source, the greater thereduction of MVC amplitude. These are logical results, as anindividual with low BMI is likely to have less body fat thannormal, which is proven to increase MVC as the electrodeis closer to the muscle. Similarly a high BMI individual willmost likely have higher body fat, and hence will have alower MVC due to an increase in electrode distance fromthe muscle. This suggests that BMI, or potentially anotherform of overweight/fat detection, may be able to compensate

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for abnormal results, and as shown in figure 7, could furtherimprove the correlation expected between circumference andMVC. Test participant B was found to have a very lowBMI (17.28), suggesting this could be the reason for theabnormally high error.

Fig. 7: Analysis of BMI on the Upper Forearm Eletrode 2results. The data shows how high and low BMI may potentialeffect results.

BMI is not the most accurate measure, as it measuresyour weight to height ratio, hence individuals who have highmuscle but low body fat will be put into the same category asan overweight individual. With obesity on the rise, detectionof body fat percentage would be a useful parameter forincreasing the stability of results.

This demonstrates how automatic circumference measure-ment could be used to establish better thresholds to fit agroup.

V. DISCUSSION

The link between MVC, muscle strength and circumfer-ence has already been established[1][12]. Our research isabout trying to establish an easy to obtain sensor position,such that automatic circumference measurements might beable to estimate MVC for automatic calibration within anacceptable error. However the stipulation of easy sensorposition will likely effect the accuracy, hence a balancebetween easy electrode placement and accuracy will beneeded.

A system estimating MVC could be beneficial to a numberof research projects [8][4] [6] in cases where thresholdscould be automatically adapted, or added to adapt to a widerpopulation. Single thresholds that are simply averaged overa group don’t work well. As shown in the previous section,using circumference based linear regression thresholds worksmuch better, however there is the possibility of outliers forwhom the system fails. Being able to detect these outliersautomatical via other human characteristics, and adjustsaccordingly, would be very beneficial to the user.

Estimating MVC from forearm circumference would workfor a large portion of the population, but it assumes that

you are healthy and have no debilitating muscle relatedproblems. Hand strength can diminish with conditions likecarpal tunnel syndrome, nerve tendon or any neuromusculardisorders. In which case, along with automatic calibrations,there should also be the option for manual calibrations usingthe MVC amplitude of the EMG signal. There are certainissues with measuring MVC through automatic circumfer-ence measurements, like fatigue for example. Circumferencedoesn’t vary considerably over the day however your levelof fatigue may, and this will effect your MVC. This is whycircumference can only be used to estimate MVC for aidingin the adjustment of thresholds.

Interestingly during one individuals experiment which pro-duced abnormally high results, the individual was noticeablynervous. Nervousness induces adrenaline and tenses yourmuscles, and would alter the skin to electrode impedance,which may have effected the EMG output. Analysis of skinimpedance may be another area to analyze to improve results.The results of the study relating MVC to circumferenceare promising, and show a good relationship over the wideethnic background and age ranges. This small study confirmsthat it is possible to estimate MVC from easy to locateelectrode positions. To further validate results a wider set oftest subjects would be needed, ideally with more individualsat the extremes, i.e. overweight, underweight, very low bodyfat, very high body fat. This would most likely negativelyeffect the relationship but could hopefully be compensatedwith body fat measurements (if accurate enough).

The load cell based circumference armband in theorycould be very accurate with a 10bit ADC, but in practiceonly 5-10mm accuracy was achieved, this was due to tworeasons. One, the elastic strap can get tangled and repeatableplacement is difficult, and two, sensor shape tended to makethe sensor a little off balance at higher circumferences. Bothissues could be easily addressed by redesigning the sensortop and the elastic strap. The ideal sensor would be muchsmaller, therefore further research is still needed to minimizethe sensor. Other sensors have been consider, for examplepiezoelectric and stretch sensors, but neither provide thenecessary functionality to accurately measure circumferencein the required situations.

It is possible that there are too many factors that contributetowards estimating MVC, circumference certainly plays arole, but perhaps not significantly enough to be used alone.MVC is often used for adjusting thresholds, consideringonly an estimation is needed to slightly increase or decreasethresholds, automatic circumference measuring could pro-vide an acceptable way to estimate thresholds, or at least becombined with 1 or 2 other measures, for example body fatpercentage, which might enhance reliability.

Using muscle circumference to estimate MVC thresholdscould be an initial step, it would be possible to use theaverage EMG signal over time to fine tune readings. Thiswould also begin to help compensate for fatigue.

All EMG systems have cross user interfacing issues,no one system has been able to adapt to every userfaultlessly[13], but there have been instances of relatively

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good cross user implementations, with results in the regionof 90%[15]. It is possible that signal processing alone is ableto establish a reasonable level of cross user operability, how-ever, adding body compositional information could improveresults further.

With simple programming a EMG armband system withautomatic circumference measurement incorporated, couldhave the bonus of being able to detect when a user wasattempting to put on or take off the armband, meaning thesystem could reduce its operation while the user is adjustingit. Once the system is removed the armband reduces to itsminimum circumference, verifying the likelihood that thedevice has been removed and could switch off after a fewminutes of inactive operation.

This confirms the usefulness of automating circumferencemeasurements for aiding in the estimation of MVC thresh-olds. With more participants a greater relationship may beobserved. Considering there is a good relationship betweenforearm circumference and force, but not as strong for EMGMVC, it is likely that more compensational measurementsmay be needed.

VI. CONCLUSION

The use of anthropometric variables is an unusual wayfor improving muscle based interfaces, and provides a newarea to be investigated. The contributions of this paperinclude finding a stronger linear relationship between MVCand upper forearm circumference than previous research,further suggesting the potential for automatic calibration ofEMG threshold. Along with demonstrating that a form ofbody fat/ BMI measure could improve results further. Thepaper also contributes the design of a new, more compact,automatic circumference measuring armband aiding in theautomatic adjustment of MVC thresholds. The new load cellbased circumference measuring armband was developed andevaluated, having the ability to measure circumference within5-10mm accuracy. The circumference armbands are by nomeans complete solutions for fully automating a muscleinterfacing system, but is an attempt to try and improve EMGcross-user implementations. Further properties may have tobe incorporated before it becomes reliable enough to beusable on a variety of body compositions.

Using anthropometric variables for estimating MVC willhopefully provide a stronger foundation to help developbetter cross user interfaces. By starting to estimate MVC, youmay be able to reduce the complexity of the user differenti-ation problem, and hopefully improve the accuracy of morecomplicated signal processing techniques. The preliminarystudies relating MVC to forearm circumference found thatby simply adding circumference data, MVC prediction couldbe improved over simple averaging predictions. With the bestresults on the upper forearm, which gave less error thanthe mid-forearm and wrist. This confirms that the currentsetup suits the upper forearm best for predicting MVC forautomatic calibration.

This research shows the potential that anthropometric vari-ables could have in simplifying cross user interfaces, so that

one day EMG interfaces may become more readily availableto the public. Once minimized, the automatic circumferencearmband would be particulary suited to calibrate a portableEMG based armband on the upper forearm. The predictionof MVC from circumference will need to include furtherparameters to improve accuracy. Body fat measurementsshow promise for aiding in compensating for the differencesbetween individuals, and may be evaluated for use in futuredesigns.

ACKNOWLEDGMENTS

This research has been financially supported by the EP-SRC grant - Global Engagement with NASA JPL and ESAin Robotics, Brain Computer Interfaces, and Secure AdaptiveSystems for Space Applications - RoBoSAS, EP/K004638/1.The 1st author is financially supported by EPSRC Stu-dentship EP/P504910/1. We would also like to thank RobinDowling for the technical support.

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