DSpace at VNU: Recognizing postures in Vietnamese sign language with MEMS accelerometers

IEEE SENSORS JOURNAL, VOL. 7, NO. 5, MAY 2007

707

Recognizing Postures in Vietnamese Sign Language
With MEMS Accelerometers
The Duy Bui and Long Thang Nguyen, Member, IEEE

Abstract—In this paper, we discuss the application of microelectronic mechanical system (MEMS) accelerometers for recognizing
postures in Vietnamese Sign Language (VSL). We develop a similar device to the Accele Glove [6] for the recognition of VSL. In
addition to the five sensors as in the Accele Glove, we placed one
more sensor on the back of the hand to improve the recognition
process. In addition, we use a completely different method for
the classification process leading to very promising results. This
paper concentrates on signing with postures, in which the user
spells each word with finger signs corresponding to the letters of
the alphabet. Therefore, we focus on the recognition of postures
that represent the 23 Vietnamese-based letters together with two
postures for “space” and “punctuation.” The data obtained from
the sensing device is transformed to relative angles between fingers
and the palm. Each character is recognized by a fuzzy rule-based
classification system, which allows the concept of vagueness in
recognition. In addition, a set of Vietnamese spelling rules has
been applied to improve the classification results. The recognition
rate is high even when the postures are not performed perfectly,
e.g., the finger is not bended completely or the palm is not straight.
Index Terms—Human computer interaction, microelectronic
mechanical system (MEMS) sensors, sign language recognition,
Vietnamese sign language (VSL).

I. INTRODUCTION

G

ESTURE recognition has been a research area which received much attention from many research communities
such as human computer interaction and image processing.
Gesture recognition has contributed significantly to the improvement of interaction between human and computer.
Another application of gesture recognition is sign language
translation. Among many types of gestures, sign languages
seem to be the most structured ones. Each gesture in a sign
language is usually associated with a predefined meaning.
Moreover, the application of strong rules of context and
grammar makes the sign language easier to recognize [13].
There are two main approaches in sign language recognition.
The former is vision-based, which uses color cameras to track
hand and understand sign language. The latter uses expensive
Manuscript received June 15, 2006; revised August 28, 2006; accepted August 29, 2006. The associate editor coordinating the review of this paper and
approving it for publication was Dr. Subhas Mukhopadhyay.
T. D. Bui is with the Faculty of Information Technology, College of Technology, Vietnam National University, Hanoi 144 Xuan Thuy, Hanoi, Vietnam
(e-mail: ).
T. L. Nguyen is with Faculty of Electrical Engineering and Telecommunication, College of Technology, Vietnam National University, Hanoi 144 Xuan
Thuy, Hanoi, Vietnam (e-mail: ).
Color versions of one or more of the figures in this paper are available online
at .
Digital Object Identifier 10.1109/JSEN.2007.894132

sensing gloves to extract parameters such as joint angles that
describe the shape and position of the hand.
With the vision-based approach, Uras and Verri [17] obtain
some success when trying to recognize the shapes using the
“size function” concept on a Sun Sparc Station. A recognition

rate of 93% for the most recognizable letter and of 70% for
the most difficult case is obtained by Lamart and Bhuiyant [9]
with the use of colored gloves and neural networks. Starner and
Pentland track hands with a color camera and then use hidden
Markov models to interpret American Sign Language (ASL)
[13]. The hands are tracked by their color. In this approach, instead of attempting a fine description of hand shape during the
hand tracking stage, only a coarse description of hand shape,
orientation, and trajectory is produced. This information is used
as the input for the hidden Markov models to understand ASL.
This approach is developed further by Starner et al. [14] with
98% accuracy. Clearly, they abandoned the idea of recognizing
hand postures.
In sign languages, many signs may look similar to each other.
For example, in the ASL alphabet, the letters “A,” “M,” “N,”
“S,” and “T” are signed with a closed fist (see [1]). At the first
sight, the postures for these five letters appear to be the same. A
vision-based system would encounter difficulties in recognizing
these postures. One approach to overcome these difficulties is to
use sensing gloves. In literature, there are numbers of work on
gesture recognitions based on sensing gloves. For example, in
order to enter ASCII characters to a computer, Grimes [4] developed the Data Entry Glove using switches and other sensors
sewn to the glove. Kramer and Leifer [8] used a lookup table
with his patented CyberGlove to recognize the 26 letters of the
alphabet. Alternatively, Erenshteyn et al. [3] used a method involving coded output, such as Hamming, Golay, and other hybrid codes together with the CyberGlove. Zimmerman invented
the VPL Data Glove [23] in order to recognize postures in different sign languages. For example, a set of 51 basic postures of
Taiwanese Sign Language was solved by Liang and Ouhyoung
[11] with probability models; and 36 ASL postures were able
to be recognized with this glove by the work of Waldron and
Kim [18] with a two-stage neural network. Those mentioned
gloves, however, are very expensive. A more affordable option

was proposed by Kadous [7]. This is a system for Australian
Sign Language based on Mattel’s Power Glove. However, because of a lack of sensors on the pinky finger, the glove could not
be used to recognize the alphabet hand shapes. With accelerometers at fingertips, Perng et al. [12] developed a text editor where
each hand gesture refers to a letter of the alphabet. For more detailed reviews of gesture recognition with sensing gloves, see
[15] and [21].

1530-437X/$25.00 © 2007 IEEE


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IEEE SENSORS JOURNAL, VOL. 7, NO. 5, MAY 2007

Together with the rapid development of advanced sensor technology, researchers are increasingly paying attention to making
this interaction more adaptive, flexible, human-oriented, and
especially, more affordable. For example, microelectronic mechanical system (MEMS) sensors such as accelerometers, geophones, and gyros [1], [10], [16], [19], [20], thanks to their small
size and weight, modest power consumption and cost, and high
reliability, allow the development of human computer interaction systems with portability and affordability. Recently, Hernandez et al. [6] proposed a system called The Accele Glove,
a whole-hand input device using MEMS accelerometers to manipulate three different virtual objects: a virtual hand, icons on
a virtual desktop and a virtual keyboard using the 26 postures
of the ASL alphabet. When using this device as finger spelling
translator, a multiclass pattern recognition algorithm is applied
[5]. First, the data are collected and analyzed “offline” on a
PC. The obtained data are transformed to vectors in the posture
space then divided into subclasses. This way, it is possible to
apply simple linear discrimination of the postures in 2-D space,
and Bayes’ Rule in those cases where classes have features with
overlapped distributions. This algorithm can be implemented as
a sequence of “if-then-else” statements in the microcontroller,
allowing a real-time processing. The application of this device

has much potential, which can be developed further to be a more
comprehensive system and for other sign languages.
In this paper, we discuss the application of MEMS accelerometers for recognizing postures in Vietnamese Sign Language
(VSL). We develop a similar device to the Accele Glove [6] for
recognition of VSL. In addition to the five sensors as in the Accele Glove [6], we place one more sensor on the back of the hand
to improve the recognition process. In addition, we use a completely different method for the classification process leading to
very promising results.
This paper concentrates on signing with postures, in which
the user spells each word with finger signs corresponding to the
letters of the alphabet. Therefore, we focus on the recognition of
postures that represent the 23 Vietnamese-based letters together
with two postures for “space” and “punctuation.” The data obtained from the sensing device are transformed to relative angles
between fingers and the palm. Characters are recognized by a
fuzzy rule-based classification system, which allows the concept
of vagueness in recognition. In addition, a set of Vietnamese
spelling rules has been applied to improve the classification results. The recognition rate is high even when the postures are
not performed perfectly, e.g., the finger is not bent completely
or the palm is not straight.
II. VIETNAMESE ALPHABET SYSTEM
Vietnamese was originally written with a Chinese-like script.
During the 17th century, a Latin-based orthography for Vietnamese was introduced by Roman Catholic missionaries. Until
the early 20th century, both orthographies were used in parallel.
Today, the Latin-based is the only orthography used in Vietnam.
The Latin-based Vietnamese alphabet is listed below:
˙ PQRSTUU
˘ Â B C D -D E Ê G H I K L M N O Ô O
˙ V
AA
XY

Fig. 1. Alphabet system in VSL.

The letters J, W, and Z are also used, but only in foreign
loan words. In addition, Vietnamese is a tonal language with
six tones. These tones are marked as follows: level, high rising,
low (falling), dipping rising, high rising glottalized, and low
glottalized.
Since the Vietnamese alphabet system is more complicated
than the English alphabet system, more signs are required for
VSL in comparison with ASL. However, it is possible to implement finger spelling of Vietnamese words similar to the ASL
system. In principle, VSL is based on the well-established ASL.
According to the ASL dictionary [1], four components are used
to describe a sign: hand shape, location in relation to the body,
movement of the hands, and orientation of the palms. A popular
concept in sign language, “posture,” is formed by the hand shape
(position of the fingers with respect to the palm), the static component of the sign, and the orientation of the palm. The alphabet
in ASL, which consists of 26 unique distinguishable postures,
is used to spell names or uncommon words that are not well defined in the dictionary.
The VSL consists of 23 based letter and some addition signs
for the accents and the tones. The 23 based letters are:
A B C D -D E G H I K L M N O P Q R S T U V X Y
In this paper, we concentrate on the recognition of postures
for these based letters. These postures are shown in Fig. 1.
III. THE SENSING DEVICE
One of the most successful MEMS sensors in the market is
ADXL202 accelerometers from Analog Devices, Inc. (www.
analog.com). The ADXL202 are low cost, low power, complete two-axis accelerometers on a single IC chip with a mea. The ADXL202 can measure both dysurement range of
namic acceleration (e.g., vibration) and static acceleration (e.g.,
gravity). The accelerometer is fabricated by the surface micromaching technology. It is composed of a small mass suspended
by springs. Capacitive sensors distributed along two orthogonal

axes (X and Y) provide a measurement proportional to the displacement of the mass with respect to its rest position. Because
the mass is displaced from the center, either due to acceleration
or due to an inclination with respect to the gravitational vector ,
the sensor can be used to measure absolute angular position. The
outputs are digital signals whose duty cycles (ratio of pulsewidth
to period) are proportional to the acceleration in each of the two


BUI AND THANG NGUYEN: RECOGNIZING POSTURES IN VIETNAMESE SIGN LANGUAGE WITH MEMS ACCELEROMETERS

709

Fig. 2. Function block diagram of ADXL202 (from Analog Devices, Inc.).

Fig. 4. The X axis and Y axis of the sensor on the finger and of the sensor on
the back of the palm.

Fig. 3. Sensing glove with six accelerometers and a basic stamp
microcontroller.

sensitive axes. The output period is adjustable from 0.5 to 10
. If a voltage output is desired, a
ms via a single resistor
voltage output proportional to acceleration is available from the
and
pins, or may be reconstructed by filtering the
duty cycle outputs. The bandwidth of the ADXL202 may be set
and
. The typical
from 0.01 Hz to 5 kHz via capacitors

noise floor is 500 g/
allowing signals below 5 mg to be
resolved for bandwidths below 60 Hz. The function block diagram of ADXL202 is shown in Fig. 2.
Our sensing device, which is shown in Fig. 3, consists of six
ADXL202 accelerometers attached on a glove, five on the fingers, and one on the back of the palm. The Y axis of the sensor
on each finger points toward the fingertip, providing a measure
of joint flexion (see Fig. 4). The Y axis of the sensor on the back
of the palm measures the flexing angle of the palm. The X axis
of the sensor on the back of the palm can be used to extract information of hand roll, while the X axis of the sensor on each finger
can provide information of individual finger abduction. Data are
collected by measuring the duty cycle of a train of pulses of
1 kHz. When a sensor is in its horizontal position, the duty cycle
to
, the duty cycle
is 50%. When it is tilted from
varies from 37.5% (.375 ms) to 62.5% (.625 ms), respectively
(see Fig. 5). In our device, the duty cycle is measured using
a BASIC Stamp microcontroller. The Parallax BASIC Stamp
module is a small, low cost general-purpose I/O computer that
is programmed in a simple form of BASIC (from Parallax, Inc.,

Fig. 5. Dependence of accelerometer output on tilt angle.

www.parallax.com). The pulsewidth modulated output of the
ADXL202 can be read directly of the BASIC Stamp module, so
no ADC is necessary. Twelve pulsewidths are read sequentially
by the microcontroller, beginning with the X axis followed by
the Y axis, thumb first. The data are then sent through the serial
port to a PC for further analyses.
IV. DATA PROCESSING

Our sensing glove produces the raw data represented as a
vector of 12 measurements, two axes per finger, and the last two
axes for the palm

.
At first, we convert our data to the angles. After that we subtract the and values of the fingers to the and values of
the palm, respectively. Note that our sensing device has a sensor
on the back of the palm, which measures the rolling and flexing
angle of the palm. By processing the data this way, we convert
the raw data into the relative angles between the fingers and the
palm. We will do the classification based on the and value


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IEEE SENSORS JOURNAL, VOL. 7, NO. 5, MAY 2007

Fig. 7. The five fuzzy sets representing the level of bending or flexing of the
fingers.

Fig. 6. An overview of the classification system.

We have found that the concept of fuzzy set is well suited
for the problem of posture classification because the posture is
normally defined in a vague way, e.g., “the index finger bends a
little bit.” Moreover, with a fuzzy rule-based system, the classification can be solved by a set of rules in natural language which
look like:
if all fingers bend maximally then

of the palm and the relative angles between the fingers and the

palm.
Our approach is different from the approach proposed in [6],
which recognizes the postures by extracting the features directly
from the raw data. There are two reasons for this. The first
reason is that the raw data are the pulsewidths which relate to
the rolling or flexing angles through cosine functions. Since the
cosine function itself is not linear, the sum of pulsewidths measured on fingers does not represent the hand shape accurately.
The second reason is that the sum is not a good function to extract the feature of hand shape. A lot of different hand shapes
can result in the same feature extracted by the sum function.
V. CLASSIFICATION
The classification system can be seen in Fig. 6. First of all,
we use the value of the palm to divide the postures into three
categories: “Vertical” which consists of the postures of letter
“A,” “B,” “E,” “H,” “I,” “K,” “L,” “O,” “R,” “T,” “U,” “V,” and
“Y”; “Sloping” which consists of the postures of letter “C,” “D,”
“-D,” and “S”; and “Horizontal” which consists of the postures of
letter “G,” “M,” “N,” “P,” “Q,” and “X.” After the postures are
divided into three categories, we use a three fuzzy rule-based
system to perform further classification.
Human beings often need to deal with input that is not in precise or numerical form. Inspired by that observation, Zadeh [22]
developed a fuzzy set theory that allows concepts that do not
have well-defined sharp boundaries. In contrast to the classical
set theory in which an object can only be a full member or a
full nonmember of a set, an object in fuzzy set theory can possess a partial membership of a fuzzy set. A fuzzy proposition of
the form “if is A” is partially satisfied if the object (usually
crisp value ) is partial membership of the fuzzy set A. Based
on that, fuzzy logic was developed to deal with fuzzy “if-then”
rules, where the “if” condition of the rules is a Boolean combination of fuzzy propositions. When the “if” condition is partially
satisfied, the conclusion of a fuzzy rule is drawn based on the
degree to which the condition is satisfied.

it is the posture of letter “A”
if all fingers does not bend then
it is the posture of letter “B”

The fuzzy rule-based system allows the classification immediately at high recognition rate without having to collect training
samples. Moreover, a new rule can be added easily for a new
posture without changing the existing rules. We would miss
out on that when using other models like neural networks and
hidden Markov models.
We model the level of bending or flexing of the fingers by
five fuzzy sets (Fig. 7): Very-Low, Low, Medium, High, VeryHigh. The fuzzy classification rules look like:
if thumb finger’s bending is Low
and index finger’s bending is Very-Low
and medium finger’s bending is Very-Low
and ring finger’s bending is Very-Low
and pinky finger’s bending is Very-Low then the
posture is recognized as letter “B”
We have created 22 fuzzy rules to classify VSL postures. The
posture of letter “G” is recognized directly with the use of the
value of the palm. With these fuzzy rules, the classification
process is done as follows. Every time we receive the data from
the sensing device, we first verify if the hand is at static position
by comparing with previous data. We wait until the hand stops
moving to start the recognition process. The preprocessed data
are used to calculate the “membership values”—the degree to
which the data belongs to the fuzzy sets. We then calculate the
degree to which the current data set matches each of the 22 fuzzy
rules. The matching degree is calculated as the product of the
membership values to which the data belongs to the fuzzy sets

BUI AND THANG NGUYEN: RECOGNIZING POSTURES IN VIETNAMESE SIGN LANGUAGE WITH MEMS ACCELEROMETERS

in the rules. Finally, the data set is recognized by the rule with
the highest matching degree.
The recognition process is enhanced by the use of Vietnamese
spelling rules. Vietnamese has a very special characteristic,
which is that all the words are monosyllabic. Moreover, Vietnamese spelling rules are very strict. The combination of
consonant and vowels must follow a set of predefined rules. In
most of the cases, a consonant cannot be followed by another
consonant. Taking advantage of these rules, when recognizing
words formed by postures of letters, we can eliminate many
misclassifications.
VI. RESULT
The system was implemented in C++, and was tested using a
total of 200 samples for each letter to measure the recognition
rate. In order to collect the samples, we have asked five different
persons to perform the posture of each letter 40 times.
Twenty out of the 23 letters reached a 100% recognition
rate. This is a very good recognition rate compared with that
of vision-based approaches. This is because in front of a
single camera, many postures look very similar. This is not an
issue with glove-based approaches because joint flexion and
finger abduction of each individual finger can be measured.
When comparing our recognition rate with other glove-based
approaches with expensive and comprehensive gloves, this is
a also a competitive rate.
In our approach, the problem arises from the three letters
“R,” “U,” and “V.” They produce ambiguity as the data represented these letters is similar. The recognition rate is 90%, 79%,

and 93%, respectively. The current two-axis MEMS accelerator
cannot detect very well the different among the postures of these
three letters. Vision-based approaches or other glove-based approaches might not suffer from this problem.
We have improved the situation by applying Vietnamese
spelling rules on word spelling in our system. After this, the
recognition rates for the three letters “R,” “U,” and “V” have
increased significantly, which is 94%, 90%, and 96%, respectively.
The novelty of our system is that the postures can also be
recognized even when they are not performed perfectly, e.g.,
the finger is not bent completely or the palm is not straight. This
is because we carry out the classification on the relative angles
between the fingers and the palm instead of the classification
on the raw data, as in [6]. This is also the result of the fuzzy
rule-based system, which allows the concept of vagueness in
recognition.
VII. CONCLUSION
In this paper, we presented work on understanding VSL
through the use of MEMS accelerometers. The system consists
of six ADXL202 accelerometers for sensing the hand posture,
a BASIC Stamp microcontroller, and a PC for data acquisition
and recognition of sign language. The classification process is
done by a fuzzy rule-based system on the preprocessed data. In
addition, we have applied a set of Vietnamese spelling rules in
order to improve the classification results. We have achieved

711

very high recognition rates. Moreover, the postures can also be
recognized even when they are not performed perfectly.
One advantage of the glove-based approaches is the potential of mobility. The glove can be used independently with an

embedded processor or by connecting wirelessly with mobile
devices such as mobile phones or PDAs. This requires our future work on wireless communication for the system, of which
the problem of energy consumption is the most challenging one.
This also requires us to port the program part of the system into
mobile devices. In the future, we also want to place more sensors of different types such as three-axis MEMS accelerator and
strain gauge into our sensing device in order to recognize more
complex forms of the VSL, as well as to recognize gestures for
other human computer interaction applications.
The approach presented in this paper can be easily applied to
other sign languages. This is done mainly by modifying the rules
in our Fuzzy Rule-Based System. There are also some other potential applications for our Sensing Glove which are: a wireless
wearable mouse pointing device, a wireless wearable keyboard,
hand motion and gesture recognition tools, virtual musical instruments, computer sporting games, and work training in a simulated environment.
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The Duy Bui received the B.S. degree in computer
science from the University of Wollongong, Wollongong, NSW, Australia, in 2000 and the Ph.D. degree
in computer science from the University of Twente,
Enschede, The Netherlands, in 2004.
Since 2004, he has been with the College of
Technology, Vietnam National University, Hanoi, as
a Lecturer. His main research interests are human
computer interaction, virtual reality, and multimedia.

Long Thang Nguyen (S’03–M’04) received the
M.S. degree from the International Institute of
Materials Science, Hanoi University of Technologies, Hanoi, Vietnam in 1998, and the Doctor of
Engineering degree from the University of Twente,

Enschede, The Netherlands, in 2004.
He has worked as a Lecturer with the Faculty
of Electronics and Telecommunications, College
of Technology, Hanoi National University, since
2004. His main activities are related to design and
application of MEMS sensors. He has been involved
in several projects such as designing of the patient monitoring system and
integrations of inertial MEMS sensors and GPS for navigation.