EMG AS A DAILY WEARABLE INTERFACE

Tiago Jo

ao Vieira Guerreiro and Joaquim Armando Pires Jorge

Department of Information Systems and Computer Engineering

INESC-ID / IST, Technical University of Lisbon

R. Alves Redol, 9, 1000-29 Lisbon, PORTUGAL

Keywords:

Electromyography, Interaction, Evaluation, Usability, Accessibility.

Abstract:

We present electromyography as an interface to control computer applications. Our prototype makes possible

for users to control any application through muscle contractions. Electromyographic device portability and

the monitoring possibility for any muscle voluntarily contracted can bring great beneﬁts at the mobility level

as in accessibility issues. Through operating system events emulation and their association with determined

muscle contractions we can replace the pointing device or some keyboard elements, achieving control of any

application.

Usability evaluations validate electromyography as a daily wearable interface where we show that it can be

used even in a mobility context. Considering accessibility, we present a synergy between applications that

ease message writing. Evaluations show that this synergy outperforms existent text-entry interfaces, based on

point and click approaches.

1 INTRODUCTION

We are used to communicate with the computer

through the use of the keyboard, pointer devices or

even by speech. All those paradigms are based on our

senses. The human body is far more active and re-

active than that. It is composed by cells, tissues and

organs which change their behavior when stimulated.

This takes us to the study of physiological signals to

increase our communicative and interactive capabili-

ties. We have focused on electromyographic signals.

Electromyography (EMG) is deﬁned as the study of

the muscular function through the analysis of the gen-

erated electric signals during muscular contractions.

The potential difference obtained in the ﬁbres can be

registered in the surface of the human body through

surface electrodes due to the biological tissues con-

ducting properties.

Our project studies the muscular activityasaninput

in order to control applications. A large set of target

muscles are available so we can interact widely with

the computer. The main goal of the project is to pro-

vide tetraplegic individuals the capability to control

a portable device (specially to be able to write and

send SMS). In order to accomplish this task we mon-

itorize muscle activity through an electromyographic

portable device, process the digital signal and emulate

certain events accordingly to the features detected.

Being able to detect and to evaluate muscular activity

in an individual gives us the possibility to associate

it with determined interface commands, thus having

the myographic signal as input. This kind of interac-

tion can also be useful to full capable individuals in a

hands-busy situation, such as in a presentation or in a

mobility context.

1.1 Related Work

The recurrent and increasing electromyography study

in medicine related areas led to a great scientiﬁc in-

vestment to improve the myographic signal acquisi-

tion and analysis process. These advances culminate

with the possibility to use portable electromyographic

devices that communicate via wireless with a process-

ing system. Portability makes it possible for any in-

dividual the transport and use of a EMG device with

great social acceptance (Costanza et al., 2004). EMG

devices portability and reduced size easily conducted

to its use in HCI with work carried through in the area

of Accessibility, Robotics, Mobile Computation and

Recognition of Gestures, among others.

(Roy et al., 1994) present a gesture-based person-

216

Joaõ Vieira Guerreiro T. and Armando Pires Jorge J. (2006).

EMG AS A DAILY WEARABLE INTERFACE.

In Proceedings of the First International Conference on Computer Graphics Theory and Applications, pages 216-223

DOI: 10.5220/0001353102160223

 SciTePress

machine interface for people with serious motor limi-

tations due to cerebral paralysis. This work, based on

gestural recognition with biomechanic and bioelectric

sensors, present many motivating results being capa-

ble to differentiate gestures through the use of neural

networks. In the same scope, (Barreto et al., 1999)

introduce a system that tries to offer the users with

serious motor limitations the possibility to use the

traditional interfaces to point and select. This sys-

tem associates face movements to mouse control, be-

ing sufﬁciently similar to the system ”Tongue Point”

(Salem and Zhai, 1997) but using myographic signals.

It is still in the accessibility context that (Eriksson

et al., 1998) lean over prosthesis control. Their work

looks for exceeding hand prosthesis existent limita-

tions when compared with the freedom presented by

a full capable hand. The authors show that through the

training of neural networks with a operating hand they

can classify the intention to make movements in the

prosthesis and with that intention they can improve

the prosthesis mechanics.

(Costanza et al., 2004), (Costanza et al., 2005) give

emphasis to the EMG usability evaluating it as a mo-

bile interaction technique.

There are some other relevant projects that use an

EMG device to control mouse, joystick or keyboard or

even a speciﬁc application, that inﬂuenced positively

the developed work (Benedek and Hazlett, 2005),

(Coleman, 2001), (Crawford et al., 2005), (Jeong

and Choi, 2003), (Manabe et al., 2003), (Rosenberg,

1998), (Surakka et al., 2004), (Tanaka and Knapp,

2002), (Wheeler and Jorgensen, 2003).

Despite of the scope, all the projects refereed in-

tend to present EMG as an input interface. In this

article we present our results and present user stud-

ies to go further and validate electromyographic de-

vices as daily wearable interfaces. Our prototype ac-

complishes the control of various computer applica-

tions and introduces a synergy between applications

that ease the text-entry task. In the next section we

present our approach. The other sections are focused

in the user evaluations, results and discussion. In the

last section we present the conclusions taken by the

user evaluations along with the work still to be done.

2 PROTOTYPE

We present a system where one can control com-

puter applications through muscle contractions. For

that purpose we need to collect the myographic sinal,

process it, extract features and create some sort of ac-

tion. In the following sections we make a detailed

presentation of our work.

2.1 EMG Portable Device

Our electromyography device collects samples at a

1000Hz sampling rate in 5 independent channels. It

has a 110dB CMRR ampliﬁer and a band pass ﬁlter

between 25 and 500 Hz with gain 1000. It is a rela-

tively small device (14cm * 8cm * 4cm) that can be

carried in a belt or pocket.

It is a portable device which communicates by a

bluetooth interface within a 100 meters range. To col-

lect the signals we use surface differential electrodes,

with 1.5 cm radius (Gamboa et al., 2004)

Figure 1: EMG Portable Device.

2.2 Signal Recording

In order to get useful information concerning the mus-

cular activity it is necessary to carefully analyze some

aspects, from technical details at the electrode place-

ment in the surface of the human body to the points

where this placement must be done. Several aspects

inﬂuence the signal quality: skin preparation, elec-

trodes placement position, electrodes ﬁxation, elec-

trodes distance and outside interferences (De Luca,

1997).

We have discarded all the skin preparation tech-

niques since we don’t think they are appropriate to

an user interface. Besides, after several tests we ob-

served good signal quality with small interference.

However, to reinforce the surface electrodes adher-

ence we created an elastic band for the neck and two

elastic bands for the forearm.

We used the 2cm distance between electrodes

which guarantees a solution of commitment (Figure

2), collecting the signal of a signiﬁcant portion of the

muscle and restricting, simultaneously, the undesired

signals to insigniﬁcant values (De Luca, 1997).

Figure 2: Electrodes placement.

Basically, the electrodes can monitorize any vol-

untarily contracted muscle. However, the signal fre-

EMG AS A DAILY WEARABLE INTERFACE

217

quencies and amplitudes are somehow different be-

tween muscles. Figure 3 presents the electrodes posi-

tion options in a frontal view. It shows surface elec-

trodes placement position in the right side and deeper

needle electrodes positions in the left side. Obviously,

we only use surface electrodes as we are studying a

wearable daily interface and want to keep users far

from pain.

The electrodes shouldn’t be placed in the motor

point where it veriﬁes a damping of the signal low

frequency components. Besides the electrodes place-

ment position it is also important to concern the ori-

entation of the electrodes in relation to muscular ﬁ-

bres (Figure 2). The imaginary line that joins the two

surfaces must be parallel to the muscular ﬁbres orien-

tation.

Figure 3: Electrodes frontal possible positions.

2.3 Signal Processing

In order to extract useful information from the digital-

ized signal we need to process it. Our signal process-

ing module is composed by a pre-processing and a

smoothing phase.

Figure 4: System Design.

The pre-processing is composed by some basic pro-

cedures that prepare the signal to be smoothed. The

signal received from the electromyography device has

a gamma of values between 0 and 4096, having this

to be adjusted, since, really, the signal oscillates be-

tween negative and positive values. The centralization

is a very basic operation and consists of deducting the

base value (2048) from the signal. After that, we add

the value to the set of received values already acquired

and, with the average calculated on these, we calcu-

late and remove the DC offset, normally existent in

EMG signal:

y(t)=f(t) − m(t) (1)

Finally, we rectify the sample. The curve recti-

ﬁcation is an operation normally used to allow the

posterior signal integration, since it transforms a

curve with positive and negative values, averaging

zero, in a curve of absolute values, all positives. Two

forms of rectifying the curve exist: eliminating the

negative values (”half-wave rectiﬁcation”) or adding

them to the positives (”full-wave rectiﬁcation”)

(Correia et al., 1992). The last process is preferable

since it keeps all the signal energy:

h(t)=|y(t)| (2)

In order to smooth the signal, we carried through an

average on a sliding window, keeping in the output

the same number of collected samples, but now

having in consideration the neighboring” samples:

g(t)=



w(i) (3)

where N is the window dimension.

We experimentally observed 50 ms as a ﬁne win-

dow dimension value as it keeps the real time impres-

sion and smoothes the signal as desired. Upper values

improve the signal quality but decrease the response

speed. Figure 5 presents the signal evolution through

the referred ﬁlters.

2.4 EMG Onset Detection

The projects mentioned in this area have strong pat-

tern classiﬁcation algorithms that give them great re-

liability but as drawback they need long training ses-

sions for each user. In our work we try to make a sim-

pler approach adaptable instantly to every user with

no training required, which we think is a major ad-

vantage. In order to detect muscle onset we could

use a ﬁxed threshold value but soon questioned this

approach since the activation value is quite different

between different persons, muscles or even in differ-

ent days. It is very difﬁcult to keep the exactly same

setup. Our system detects onset detection with an ap-

proach where threshold is estimated as a multiple h of

standard deviations (Staude et al., 2001).

GRAPP 2006 - COMPUTER GRAPHICS THEORY AND APPLICATIONS

218

Figure 5: Signal Processing.

2.5 Event Generation

Instead of keeping our application enclosured we

thought it would be advantageous to be able to con-

trol any computer application with the interface cre-

ated. With that purpose, our prototype acts like a

background monitor that processes the signal and as

muscle activations are detected it launches operating

system events (mouse movements, mouse clicks or

keyboard events). The user can set up the events for

any input channel. As we simulate operating system

input events they can be used in any active applica-

tion. With this system we can control the ”Desk-

top”, open applications, work within those applica-

tions, and change between them, as long as they have

a limited number of input events. It can actually be

used in a Linux or Windows desktop. Full desktop

control is achieved.

3 METHODOLOGY

We conducted several experiments to assess the us-

ability of myographic activity as as interaction modal-

ity. For that purpose, besides the experiments to vali-

date the interaction speed and accuracy, we focused

our attention in the interface robustness as a daily

wearable interface.

3.1 Speed and Accuracy

In order to evaluate the speed and accuracy of our pro-

totype and validate EMG as an input interface we de-

veloped a simple test application. It is quite similar to

the one used by (Barreto et al., 1999) and consists in

a point and click timed exercise. The setup is created

with enough electrodes to emulate mouse moving di-

rections and left-click.

We developed a simple OpenGL application with

a Start Button (presented in a corner position) and a

Stop button presented in the middle of the screen. The

experiment consists in:

a) Clicking Start Button, where a timer is activated;

b) Moving the cursor towards the Stop button, with

any trajectory;

c) Clicking Stop Button, and the time is presented

to the user and saved.

The Start Button dimensions are always 8,5 x

8,5mm but there are four Stop Button dimensions (8.5

x 8.5mm; 12.5 x 12.5mm; 17 x 17mm; 22 x 22mm).

We made 80 evaluations, 20 of each for every Stop

Button size. The Start Button changed between the

four corners.

The users were equipped with two pairs of elec-

trodes in each forearm (four directions) and another

pair near one eye to detect blinking (click emulation).

3.2 Wearability

(Costanza et al., 2005) give relevance to EMG tech-

nology in the context of the mobile computation men-

tioning it as a subtle interface translated in a great so-

cial acceptance. They are based on the fact that in-

dividuals who use the system are able to interact pri-

vately without disrupting the environment that encir-

cles them. This work is mentioned by the motiva-

tional ideals related to the use of EMG with mobile

devices. It evaluates the EMG usability while walk-

ing and making contractions of different durations.

However, (Costanza et al., 2005) use only one input

channel for simple subtle intimate response events.

Our evaluation method tries to validate EMG wear-

ability and mobility but with a more complex proto-

type where there are several monitorized input chan-

nels/muscles and several corresponding actions previ-

ously selected. The aim of this experiment is to eval-

uate if the system responds as it is expected even in

standing and walking conditions.

To evaluate the system’s correct response we de-

signed a walking circuit (similar to Constanza’s)

which the user has to follow as he responds to orders.

Several variants were tested from the Walking with no

contractions setup to the walking with 4 contractions

involved. The variations are:

• Walking with no contractions.

EMG AS A DAILY WEARABLE INTERFACE

219

• Standing with stimulus response.

• Walking with stimulus response.

The users were equipped with two pairs of elec-

trodes in each forearm (four directions) and another

pair in one eye’s zone (click). Another setup was cre-

ated with one pair of electrodes in each side of the

neck (Figure 6). The ﬁrst setup is directed to mobility

issues as the second is directed to quadriplegic users.

Figure 6: Electrodes placement.

3.3 Daily Control

Electromyography can bring great beneﬁts in situa-

tions where the traditional input devices are not avail-

able. They could be unavailable due to individuals

impairments or to the situation context.

Concerning accessibility, we can look at EMG as

a wide alternative since it can explore any voluntarily

contracted muscle. Thinking in quadriplegic,i.e. we

can attach surface electrodes to their neck and make

possible a pointer control in a computer.

Concerning full capable individuals in a hands-

busy context we can look at EMG as an alternative

to control a mobile device.

There are great beneﬁts in EMG as a daily control

interface but we need to validate its use. To evaluate

the continuous use of our prototype (several hours)

we asked the users to control the pointer device in the

computer with muscle contractions. With that pur-

pose and to detect any failure we removed the user’s

regular pointing device.

The users were asked to interact freely with the

computer for two hours. We asked for regular activ-

ity demanding several movements and clicks. This

experiment evaluates the prototype’s usability and the

EMG signal quality in a large time-scale.

This evaluations were performed with the two elec-

trodes placement setups already refereed (forearm

and neck).

3.4 Dasher and Accessibility

The main goal of our research around electromyo-

graphy is to provide a writing mechanism for

quadriplegic users. This capability will be merged

with the control of mobile devices main tasks. In gen-

eral, projects around EMG bet in a point and click

approach, which is inappropriate to the writing activ-

ity (very slow). We purpose a synergy between ap-

plications where a pointer is continuously controlled

by myographic activity, which appears to be a faster

and efﬁcient approach. Dasher (Ward et al., 2000) is

a text-entry interface based in a zooming technique.

This application was developed considering situations

or users associated with an incapability to write in

a keyboard. The user basically navigates in a ”sea

of letters” which appear accordingly to word predic-

tion techniques. It allows two-dimensional and one-

dimensional control.

Figure 7: Dasher Application.

The users were asked to write the sentence ”Dasher

is a ﬁne text entry interface and I enjoy it”. This eval-

uations were performed with the forearm setup and

neck setup (Figure 8) with only two electrodes as the

application gives the one-dimensional control possi-

bility.

We also tested the forearm position setup and asked

the users to write the sentences using Windows On-

Screen Keyboard to compare our synergy navigation

application with point and click approaches. We used

the same goal sentence.

4 RESULTS

The tests were carried through in a Pentium IV

portable computer, with 512 MB RAM and a 17”

color monitor. To collect the real time signal we

GRAPP 2006 - COMPUTER GRAPHICS THEORY AND APPLICATIONS

220

Figure 8: Neck Elastic Band.

used the electromyographic device described in Sec-

tion 2.1.

The system was tested with three subjects, with 19,

24 and 51 years. All of them were used to deal with

computers. The studies took several hours with each

user because we needed to test the system and the

paradigm robustness. The presented results are also

preliminary as we want to focus our development in

accessibility, and so further user evaluations will be

effectuated with tetraplegic individuals.

4.1 Speed and Accuracy

Table 1 shows the average values taken for each sub-

ject to complete the 80 trials. The subjects required

an average of 7.5 seconds to achieve the experience

goal.

Table 1: Speed and Accuracy trial times.

Subject 1 2 3

Average Trials Time (s) 7.505 6.982 7.903

Standard Deviation 2.805 2.345 3.014

Before the experiment the users familiarized with

the system for two minutes. Familiarization was a

very fast task since the users understood the relation

with the mouse movements normally executed.

This experiment gave the users the necessary con-

trol of the device to complete further evaluations.

4.2 Wearability

This experience intended to test the system in stand-

ing and walking conditions while responding to voice

impulses. The users were already familiarized with

the system due to ﬁrst experiment trials.

One of the users had one false positive in the Walk-

ing with Stimulus Response task. The other two had

no false positives. The false positive was due to wire

misplacement. No false negatives were detected in

any of the users’ experiments.

4.3 Daily Control

The results to this test are only qualitative. The users

achieved total control of the interface and performed

their usual tasks (Web Browsing, Messaging, Docu-

ment Opening and Closing,...). Some of them noticed

mouse right-click missing. They also noticed a lack

of productivity since they couldn’t get the same per-

formance as in their usual interaction mode. However,

the users were impressed with the control achieved by

the interface and found it usable. In the pos-analysis

we noticed an improvement of the signal quality (less

noise).

4.4 Dasher and Accessibility

The experiment results are presented in Table 2. In

order to understand the evaluation we need to deﬁne

exactly the meaning of every metric:

• Error: an error is detected when the user misses a

letter and has to come back. Some of these errors

may be users fault, i.e. skipping a letter or a space

by distraction.

• Time: time until the user ends his sentence cor-

rectly.

Table 2: Average Text-entry trial results.

Task Errors Time(s)

Dasher/Forearm 0.00 124

Dasher/Neck 0.33 200

On-Screen Keyboard/Forearm 0.33 480

All the users succeeded and were capable to write

the entire message. The errors detected were related

to user’s distraction, i.e. skipping letters and having

to go back in the writing. There were no errors in

the forearm control + Dasher task. One of the users

made an error in the neck control (missed a letter) but

was capable to go back and complete the trial. We had

one error in the On-Screen Keyboard (hitting between

letters).

5 DISCUSSION

The results obtained in the several experiments

present Electromyography as an alternative interface

to the traditional ones.

Our approach seems to be quite efﬁcient compared

to others, i.e. in the Speed and Accuracy test we

duplicated an experiment already made by (Barreto

et al., 1999) and the results are quite better. The sub-

jects in our trials required around 7.5 seconds to move

the cursor from the corner button to the center button

EMG AS A DAILY WEARABLE INTERFACE

221

and performing a Left-Click as Barreto had an aver-

age result of 16 seconds. One of Barreto’s suggestion

was to use the prototype with On-Screen Keyboard

to entry text. We presented a synergy between ap-

plications that outperforms the On-Screen Keyboard

scenario: our approach averaged 124 seconds against

480 seconds in the keyboard. Both the Speed as the

Dasher results are quite interesting and present elec-

tromyography as an auxiliary interface for impaired

individuals. These tests included the writing through

neck movements which were successful. Electromyo-

graphic interaction is an opportunity for tetraplegic

individuals and we improve this opportunity with a

faster and accurate approach.

Our test users used the system for hours and eval-

uated its daily utilization. Even if it is slower than

their normal input devices it is certainly viewed with

another enthusiasm by people incapable of using key-

board or normal pointing devices. Nowadays, eye-

trackers already provide this kind of control for users

with special needs but, although they present a faster

movement of the cursor, they also present serious lim-

itations like the difﬁculty to accurately control the

cursor, or its loss of calibration during use. The

cost of an Eye-tracker is also a disadvantage when

compared with an electromyographic device (Barreto

et al., 1999). The system becomes even more interest-

ing when we analyze the scenario where a tetraplegic

user in his wheelchair and wants to make a call from

his cell phone or any other task in a mobile device.

The displays are way to small for any tracker and an

alternative is required.

To validate electromyography as an input device

we had to evaluate it in a wearable basis. Wether in

Accessibility (in a wheel chair) or in Mobility issues

(Walking while controlling some kind of application

in a mobile device) the system is subjected to various

movements. We conclude by our experiments that as

long as the montage is made carefully and the wires

are ﬁxated the system acts as expected. This is a par-

ticularly lesser problem with tetraplegic users as their

motor capabilities are reduced.

6 CONCLUSIONS

This paper presents EMG as a daily wearable inter-

face. We presented a prototype where users can con-

trol computer applications through muscle contrac-

tions.

Our evaluations revealed the prototype as a fast

and accurate input interface alternative. We have also

shown that EMG can be used continuously even in

a mobile context without unexpected behavior. This

characteristic potentiates the joint use of electromyo-

graphy with mobile devices in order to work in a

hands-busy situation or to interact without disrupting

the surrounding environment.

We have also presented a synergy between our pro-

totype and Dasher that eases the text-entry function.

This is a major contribution for users with special

needs. The capability to monitorize any voluntarily

contracted muscle gives us the ability to adapt the sys-

tem to several impaired individuals and their special

needs.

6.1 Future Work

Our work will continue with the migration of the

prototype to a mobile device. We intend to con-

tinue our development in the Accessibility area, fo-

cusing on quadriplegic individuals. Our goal is to give

quadriplegic the basic control of a cell phone, includ-

ing messaging, with and EMG device and a mobile

device attached to a wheel chair. Further user studies

will be executed in that context.

We also intend to make efforts in the signal

processing so we can recognize more movements

with the same monitorized muscles. This will im-

prove the interaction possibilities and number of em-

ulated events.

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