The 18th IEEE International Symposium on
Robot and Human Interactive Communication
Toyama, Japan, Sept. 27-Oct. 2, 2009

ThC3.3

Prediction of head-rotation movements using neck EMG signals forauditory tele-existence robot “TeleHead”
Ken-ichi Morishige, Takayuki Kurokawa, Masayuki Kinoshita,
Hironobu Takano, and Tatsuya Hirahara

Abstract— A number of works have been reported on robot
control using EMG signals. Control of robots, wheelchairs, andrehabilitation aids using the arms, hands or legs by EMG
signals has been quite popular and effective. However, few
works have dealt with head-movement control using neck
EMG signals. We have built a model that estimates continuous
human head movement from neck EMG signals. Our proposedmodel, which considered not only static but also dynamic
effects, effectively suppressed the over/undershoot, andpredicted head-rotation movements well. This result indicatesthat the proposed model has the potential to reconstruct theobserved data from neck EMG signals properly.

A

I. INTRODUCTION

virtual reality provides various kinds of sensory
information to make users feel that they are in aparticular “place”. The auditory tele-existence robot is one
kind of the virtual auditory display systems and was built
based on the concept that remote 3D sound space
reproduction can be best achieved by using a steerableuser-like dummy head that tracks head movement([1-3], Fig.
1). This robot quietly synchronizes with the user's head
movements in real time, providing the user with a perfect
3-Dimentional virtual auditory space. Because, perfect
dynamic binaural signals are presented at the user’s ears.
In order to achieve a believable sense of reality, it isessential for the system to calculate and display sensory
information fast enough to fool the senses. Current motion
sensors (e.g. acceleration and magnetic sensors), whichmonitor the head movement of the user, have enoughcapability for capturing fast movements but have some
temporal delay (Fig. 1(b)). However, time delay inevitablyexits because of controlling the mechanical dummy-headrobot. The total amount of the time delays is 100 ms, which
Manuscript received March 15, 2009. This work was supported by the
Toyama New Industry Organization.
K. Morishige is with Toyama Prefectural University, 5180 Kurokawa
Imizu-shi, Toyama 939-0398, Japan (phone: +81-766-56-7500; fax:
+81-766-56-8030; e-mail: kmorishi@ pu-toyama.ac.jp).
T. Kurokawa is with Toyama Prefectural University, 5180 Kurokawa
Imizu-shi, Toyama 939-0398, Japan (e-mail: s954008@st.pu-toyama.ac.jp).
M. Kinoshita is with Tateyama Kagaku Industry Co., Ltd., 30
Shimonoban,
Toyama-shi,Toyama930-1305,Japan(masa-k@tateyama.or.jp).
H. Takano is with Toyama Prefectural University, 5180 Kurokawa
Imizu-shi, Toyama 939-0398, Japan (e-mail: takano@pu-toyama.ac.jp).
T. Hirahara is with Toyama Prefectural University, 5180 Kurokawa
Imizu-shi, Toyama 939-0398, Japan (e-mail: hirahara@pu-toyama.ac.jp).

978-1-4244-5081-7/09/$26.00 ©2009 IEEEis larger than the threshold of detecting the motion delay, i.e.
60 ms [4]. The delay could somehow deteriorate thereproduced virtual 3D auditory space.
For avoiding this problem, the use of electromyogram(EMG) is one of the alternative candidates for monitoring auser’s head-rotation movements (Fig. 1(c)). Because EMG
is observed about 100 ms before the onset of the muscle
movement. A number of works have been reported onhuman-robot interface using EMG signals [5]. Control ofrobots [6-8], wheelchairs [9-11], and rehabilitation aids [12]using the arms, hands or legs by EMG signals have beenquite popular and effective. Some studies have also dealt
with head-movement control using neck EMG signals. For
example, several head motions could be detected fromcharacteristics of the neck EMG signals, and their motions
are converted into operation commands [9] [11]. Headpostures were estimated from neck EMG signals using theartificial neural network model [13]. These studies
uncovered several motion patterns from the EMG signals.
However, there are infinite numbers of spatio-temporal
motion patterns, and it is impossible to recognize all types ofhead movements. For building a practical human-robot
interface for “TeleHead”, it is necessary to estimate the timeseries of head-rotation movements.
This paper proposes a simple and effective method for
estimating continuous human head-rotation movementsfrom neck EMG signals. Our proposed model, which
considers not only static but also dynamic effects,
effectively suppresses the over/undershoot, and predicts
head-rotation movements well. This result indicates that the
proposed model has the potential to reconstruct the observed
data from neck EMG signals properly.

II. METHODS
A. Subjects
Ten male subjects, aged 21-35 years old, participated in
the experiment. All subjects participated in the threeexperimental tasks. The institutional ethics committee
approved the experiment, and the subjects gave informed
consent prior to participation.

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B. Experimental settings
Subjects sat on chairs, and their shoulders were fixed tothe back of the chairs with a harness. Three LCD monitors
were placed in front of the subjects, and gave the subjects
the visual information about the movement tasks.
Head-direction angle was recorded using a motion sensor(Flock of Birds, Ascension Technology Corporation) andsampled at 35Hz. This motion sensor was horizontally
attached to the head of the subjects with a headband(Katyusha).
Surface EMG activity was recorded from four mainmuscles for head-rotation movements: right and left
sternocleidomastoid muscles and right and left trapezius
muscles. EMG signals were recorded with pairs ofsilver-silver chloride surface electrodes in a bipolar
configuration. Each signal was amplified by BioSemi
Active-Two amplifiers (BioSemi, Amsterdam, The
Netherlands) and sampled at 2048 Hz.

Fig. 1. Outline of “TeleHead” system. (a) Steerable dummy-head (b)Current interface using motion sensors. (c) EMG interface.

C. Head-direction angle estimate by EMG
1) Static model
Muscle activations produce tensile forces. Head-direction
angle depends on the static balance between the left- andright-ward tensile forces. We assumed that the neck EMG
signal, which reflects muscle activation levels, has somecorrelation with the head-direction angle. In order toestimate the head-direction angle from neck EMG signals,
we used a linear regression model (Fig. 2(a)). This model isgiven byN

θ s = ∑ β s X is + E s ,

(1)

i =1

s

s

where N is the number of channels (N=4). X 1 , X 2 ,X 3s , and X 4s are static EMG signals of left and rightFig. 2. Diagrams of estimation models. (a) indicates static model (b)indicate dynamic model, which considers not only static but also dynamic
effects.

sternocleidomastoid muscles and left and right trapezius
muscles, respectively. βs is partial regression coefficient, Es
is residual error for the static model, and θs is the estimatedhead-direction angle. The suffix “s” represents a parameterused by the static model.
2) Dynamic model
Unbalanced tensile forces between left- and right-wardneck muscles cause a head-rotation movement.
We assumed that muscle activations consist of two
components. The first is a static effect that contributes toholding the current posture. The latter is a dynamical effectthat contributes to accelerating or decelerating thehead-rotation movements.
In general, EMG signals have a correlation withmovement acceleration. Angle acceleration is assumed to be
expressed as follows:1081

θ e (t ) = θ ′′(t )N= ∑ β e X i (t − d ) + E e ,

(2)

i =1

Fig. 3. Task setting (Experiment I).

where d means time delay between the EMG and realized
rotation angle. The suffix “e” represents a new parameterused by the dynamic model.
To compensate for the effect of dynamics, estimatedhead-direction angle θ (t ) is expressed as follows:θ (t ) = θ s (t ) + θ e (t )N= ∑ (β s + αβ e ) X i (t − d ) + E ,

(3)

i =1

where N=4, Xi is EMG signal, β e is partial regression
coefficient, and E is residual error for the dynamic model.
Here, α is a constant number. This model represents the
dynamical effects as a parameter β e, thus giving it theability to estimate the effect of dynamics properly.

Fig. 4. Schematic illustration of a trial sequence (Experiment II).

Fig. 5. Schematic illustration of a trial sequence (Experiment III).

D. Movement tasks
1) Experiment I (postural maintenance)
The first task was to hold an instructed head-direction
angle for a brief period (about seven seconds). Fifteen
head-direction angles were prepared (0°, ±10°, ±20°, ±30°,±40°, ±50°, ±60°, and ±70°, Fig. 3). The instructed andobserved angles were displayed on the LCD monitors.
Subjects were requested to hold each instructed
head-direction angle while watching the monitors. Ninety
trials (15 directions × 6 sessions = 90 trials) were recorded
and used for analysis. Subjects were allowed to take a brief
rest after each session.
2) Experiment II (rotating movements)
The second task was to rotate the head direction slowly.
Subjects performed two types of movements: right-leftward
and left-rightward head rotational movements (Fig. 4(a)).
Three seconds before the movement start cue, visual
information about the task type was presented on the screen(“→” for right-leftward and “←” for left-rightward
movement tasks). After the start cue, subjects began to rotate
their head direction for four seconds and maintain theirangles for two seconds (Fig. 4(b)).
One session consisted of thirty repetitions of a trial fortwo movement patterns. Each subject carried out 60 trials (2types × 15 trials × 2 sessions = 60 trials). Two types of thetasks were presented in a pseudorandom order.
3) Experiment III (different speed movements)
The third task was to execute head-rotation movements of
two different rotation speeds: quick and slow. In the quick
movement, subjects rotated their heads from the front to an
instructed direction for one second. In the slow movementtask, subjects rotated them for two seconds. After the
movement, subjects maintained their angles for two seconds1082

(2 speeds × 2 directions ×15 trials ×2 sessions= 120
trials).
E. Data Analysis
Angle data were digitally filtered by a third-order
Butterworth filter with an upper cutoff frequency of 5 Hz.
Derivatives of the angle data were computed by applying athree-point local polynomial approximation.
The noise signals of the angle sensor (Flock of Birds)
were removed using the band-stop filter 201-213 Hz. EachEMG signal was digitally filtered at 20 Hz (high-pass) and
1,000 Hz (low-pass) to remove some of the noise component.
The filtered EMG signals were full wave rectified and thensmoothed using a moving average technique that averaged
every 101 points of data.
Estimated angles using Eqs. (1) and (3) were smoothed bya third-order low-pass Butterworth filter at 1Hz cut-off.
III. RESULTS
Fig. 6. Mean rectified EMG of each muscle for each subject andhead-rotation angle. (a)-(d) indicate mean rectified EMG of left and rightsternocleidomastoid, and left and right trapezius muscles, respectively.
Open circles indicate the observed value for each head-rotation angle. Gray
areas indicate the angular range of ten degrees.

Fig. 7. Example of observed and estimated angle data (left-rightward
movement). The black line indicates the observed angle data. The dashedand gray lines indicate the estimated angle data using static and dynamic
models, respectively.

(Fig. 5). Either one of the rightward and leftward directions
was randomly presented on the screens (the same protocol as
Experiment II).
We recorded 30 trials for each speed and each direction.
One session consisted of thirty repetitions of a trial for two
movement directions. Altogether, 120 trials were obtained

A. Relationship between head-rotation angle and neck
EMG signals
First, we investigated the relationship between thehead-rotation angle and neck muscle activation levels. In therightward rotation task, mean EMG signals of leftsternocleidomastoid and right-trapezius muscles wereincreased (Fig. 6(a) and (d)). In the leftward rotation task,
the activation levels of right-sternocleidomastoid andleft-trapezius muscles were increased (Fig. 6(b) and (c)).
The intensity of the sternocleidomastoid muscles had atendency to be larger compared to that of the trapezius
muscles. A higher variance was present at large angles
among the subjects (e.g. ±70°).
B. Estimation of head-direction angle from neck EMG
signals
We estimated head direction angles from neck EMG
signals. The first step in data analysis was a multiple linear
regression using the model of Eq. (1). This analysis yielded
coefficients for each EMG signal using the least square
method. Next, predicted angles were computed by Eq. (1).
The quality of the prediction was quantified by calculating
the R-square coefficient between actual and predicted data.
Finally we carried out cross-validation analysis to evaluate
the robustness of the results. The trial data of the six sessions
were equally divided into three data sets, and two of themwere used for the calculation of weighting coefficients as
training data sets. Then the angle predictions were computedfor the other one-third as a test data set.
The R-square coefficients between the actual andpredicted angles were high (first one-third of the data set: R2= 0.87 ± 0.06 [mean±SD], second one-third: R2 = 0.86 ±
0.08, final one-third: R2 = 0.86 ± 0.06).

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We investigated whether there were any differencesamong the R-squares calculated from the three test data sets.
A randomized block ANOVA could not find any significant
differences among them (F(2, 18) = 1.06, p = 0.39).
These results indicate that, in this static task, a simple
linear regression model can adequately predict
head-direction angles with robustness from neck EMG
signals.

Fig. 8. Comparison of AIC for each test data set. Data represent means ±SD. To evaluate the two models, paired t-test was conducted for each test
data set. * and ** denote significant levels p<0.01 and p<0.001,
respectively.

Fig. 9. Examples of observed and estimated angle data. (a) and (b) indicate
the results of the quick and slow movement tasks, respectively.

C. Reconstruction of head rotation movement from neck
EMG signals
When we rotate our heads, it requires a larger force toaccelerate or decelerate the head-rotation movements. Thesemovements inevitably involve dynamics to overcome theviscous-inertial load. If reconstructing the head-rotation
movement information from neck EMG signals, it isnecessary to compensate for the effect of dynamics.
In general, the mean levels of muscle activations have atendency to increase with movement acceleration. This
paper assumes that estimation errors of Eq. (1) arising fromthe dynamical effect have a correlation with movement
acceleration and deceleration, and thus we derived Eq. (3).
This method concerning the effect of dynamics wasapplied to estimation of the time series of head-rotation
movement. Figure 7 shows typical trial data from a single
subject. The black line shows the observed data. The dashedline shows the estimated data using the static model, while
the gray line shows the estimated data using the dynamic
model.
When using the static model, estimated angle data have atendency to overshoot and undershoot at movement onset
and offset, respectively (Fig. 7, dashed line). When using
only the static model, the observed data were unable toreconstruct the movement information correctly.
In contrast, the dynamic model, which considered not
only the static but also the dynamic effects, effectively
suppressed the over/undershoots. This result indicates thatthe proposed model has the potential to reconstruct theobserved data from neck EMG signals properly.
In small head-rotation angles, errors between theobserved and estimated angles using the both models werelarge because these angles require less muscle activations
for head-rotation movements.
For the efficient model discrimination methods, Akaike’s
information criterion (AIC) was calculated [14]. The AIC ofthe dynamic model was significantly smaller than that of thestatic model ([first half] paired t-test: t(9) = 4.56, p < 0.001,[last half] t(9) = 4.43, p < 0.001, Fig. 8).
Next, we evaluated the robustness of these results bycross-validating the predictions between the first and second
halves of the data. One half of the data were used for the
calculation of weighting coefficients using the dynamic

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mode Eq. (3), and then angle predictions were computedfrom the other half of the data. This analysis is important as atest of the feasibility of this approach for controlling theauditory tele-existence robot “TeleHead” in real-time.
The Pearson’s product-moment correlation coefficients
between the actual and predicted angles were relatively high([first half]: r = 0.55 ± 0.17, [last half]: r = 0.58 ±0.14).
We investigated whether there were any differences
between the correlation coefficients calculated from the twotest data sets. Paired t-test could not find a significant
difference between them (t(9) = 1.46, p = 0.91). There wasalso no significant difference between the AICs calculated
from the two test data sets (paired t-test: t(9) = 1.39, p =0.90).
This dynamic model has sufficient ability to predict thetime series of head-rotation angles, and has the potential tocontrol the robot in real time.
D. Generalization ability of the dynamic model
For predicting various kinds of head-rotation movementsfrom neck EMG signals, generalization ability is one of theimportant elements. In order to evaluate this ability, thedynamic model was applied to the EMG data obtained fromunknown tasks to the model itself. The first half of the data,“rotating movements,” were used for the calculation ofweighting coefficients using the dynamic model Eq. (3), andthen angle predictions were computed from the data, “quick
and slow movements.”
Figure 9 shows examples of trial data from a single
subject. When using the static model, estimated angle data
have a tendency to mistakenly predict larger angles than theactual ones (Fig. 9(a), dashed line). In contrast, the dynamic
model could suppress the effect of dynamics(over/undershoot), and correctly compensated for themagnitudes of head-rotation angles (Fig. 9(a), gray line).
This tendency was observed in the different rotation-speed
tasks (slow movements, Fig. 9(b)).
The AIC of the dynamic model was significantly smaller
than that of the static model ([quick movement] paired t-test:
t(9) = 4.22, p < 0.01, [slow movement] t(9) = 4.18, p < 0.01,
Fig. 8). That indicated that the dynamic model was more
efficient for head-rotation movements compared to the staticone.
The correlation coefficients between the actual andpredicted angles were also high ([quick movement] r = 0.72
±0.25 [slow movement] r = 0.79±0.23).
These results indicate that the dynamic model has a highgeneralization ability of estimation from neck EMG signals.

indicates that the proposed model has the potential toreconstruct the observed data from neck EMG signals
properly.
A. Implementation of human-robot interface
We implemented this proposed method to construct aninterface with an acoustical telepresence robot (TeleHead)[1-3]. We confirmed that the proposed method enables us tocontrol the robot effectively. Although there was someover/undershoot on estimated head-rotation angle by the
dynamic model, the TeleHead worked without problem.
Furthermore, the estimated angle preceded actual head
movement just as EMG signals precede actual muscle
movement, which almost perfectly compensates formechanical and control delays of the head following
movement of the TeleHead.
B. Computational cost
The only time-consuming part of the proposed method is
calculating the weighting coefficients of each sensor. After
doing so, the calculation of the prediction is almost
instantaneous. The proposed model is superior not only interms of prediction abilities but also computational
performances, making it very easy to implement in the actual
human-robot interface.
C. Comparison among other estimation methods
The proposed dynamic model is the first step to control
the TeleHead by using neck EMG signals. To evaluate theadvantage of the proposed model, it is necessary to comparewith other EMG-based motion decoding methods, such as
linear state space models, support vector machines, etc. It is
an issue in the future.
D. Preprocessing
The magnitudes of raw EMG signals will change
drastically for a number of reasons, such as the state of theelectrodes, the distance between the electrodes, theconfiguration of muscles, skin condition, etc. Raw EMG
signals normalized by maximum voluntary contraction(MVC) have a physically meaningful value, and the use of it
would be expected to make our results in Fig.6 clearer.
E. Improvement of estimation in front
The errors between actual and predicted angles of Fig. 7
were large in relatively small head-rotation angles. Becausemuscle activation levels are less in front, that would cause
the less precise estimation. To solve this problem, Moon etal. suggested that the image observation was used [15]. Thishybrid estimation method might improve the precision ofangles in front.

IV. DISCUSSION
We have built a model that estimates continuous humanhead movement from neck EMG signals. Our proposedmodel, which considered not only static but also dynamic
effects, effectively suppressed the over/undershoot, andpredicted head-rotation movements well. This result

ACKNOWLEDGMENT
We are grateful to A. Tojo for assistance during theexperiments.

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