12-04-2013, 02:46 PM
An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition
[attachment=52988]
Abstract
This paper presents an accelerometer-based digital
pen for handwritten digit and gesture trajectory recognition applications.
The digital pen consists of a triaxial accelerometer, a
microcontroller, and an RF wireless transmission module for sensing
and collecting accelerations of handwriting and gesture trajectories.
The proposed trajectory recognition algorithm composes of
the procedures of acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature extraction. The
algorithm is capable of translating time-series acceleration signals
into important feature vectors. Users can use the pen to write digits
or make hand gestures, and the accelerations of hand motions
measured by the accelerometer are wirelessly transmitted to a
computer for online trajectory recognition. The algorithm first
extracts the time- and frequency-domain features from the acceleration
signals and, then, further identifies the most important
features by a hybrid method: kernel-based class separability for
selecting significant features and linear discriminant analysis for
reducing the dimension of features.
INTRODUCTION
EXPLOSIVE growth of miniaturization technologies in
electronic circuits and components has greatly decreased
the dimension and weight of consumer electronic products,
such as smart phones and handheld computers, and thus made
them more handy and convenient. Due to the rapid development
of computer technology, human–computer interaction
(HCI) techniques [1]–[3] have become an indispensable component
in our daily life. Recently, an attractive alternative,
a portable device embedded with inertial sensors, has been
proposed to sense the activities of human and to capture
his/her motion trajectory information from accelerations for
recognizing gestures or handwriting.
RELATED WORK
Recently, some studies have focused on the development of
digital pens for trajectory recognition and HCI applications.
For instance, an alternative method of conventional tablet-based
handwriting recognition has been proposed by Milner [15]. In
his system, two dual-axis accelerometers are mounted on the
side of a pen to generate time-varying x- and y-axis acceleration
for handwriting motion. The author employed an HMM
with a bandpass filtering and a down-sampling procedure for
classification of seven handwritten words. The best recognition
rate is 96.2% when the number of states of the HMMis equal to
60. Oh et al. [16] presented a wandlike input device embedding
a triaxial accelerometer and a triaxial gyroscope for online 3-D
character gesture recognition. Fisher discriminant analysis was
adopted, and different combinations of sensor signals were used
to test the recognition performance of their device. When all six
axes raw signals were used as inputs of the recognition system,
the recognition rate was 93.23%.
TRAJECTORY RECOGNITION ALGORITHM
The block diagram of the proposed trajectory recognition
algorithm consisting of acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature
extraction is shown in Fig. 3.
In this paper, the motions for recognition include Arabic
numerals and eight hand gestures. The acceleration signals of
the hand motions are measured by a triaxial accelerometer and
then preprocessed by filtering and normalization. Consequently,
the features are extracted from the preprocessed data to represent
the characteristics of different motion signals, and the
feature selection process based on KBCS picks p features out of
the original 24 extracted features. To reduce the computational
load and increase the recognition accuracy of the classifier, we
utilize LDA to reduce the dimension of the selected features.
The reduced feature vectors are fed into a PNN classifier to
recognize the motion to which the feature vector belongs. We
now introduce the detailed procedure of the proposed trajectory
recognition algorithm as follows.
Signal Preprocessing
The raw acceleration signals of hand motions are generated
by the accelerometer and collected by the microcontroller.
Due to human nature, our hand always trembles slightly while
moving, which causes certain amount of noise. The signal
preprocessing consists of calibration, a moving average filter,
a high-pass filter, and normalization. First, the accelerations
are calibrated to remove drift errors and offsets from the raw
signals.
EXPERIMENTAL RESULTS
In this section, the effectiveness of trajectory recognition
algorithm is validated by the following two experiments:
1) handwritten digit recognition and 2) gesture recognition. The
proposed trajectory recognition algorithm consists of the following
procedures: acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature extraction.
We collected the acceleration signals of the two experiments
from ten subjects (four females, six males; age 24.1 ± 2.13
years old) in a laboratory environment. We used different
combinations of feature selection and extraction methods and
employed PNN to recognize handwritten digits and hand gestures.
In addition, we compared the recognition results of the
PNN trained by the features from different feature engineering
methods with those of feedforward neural networks (FNNs).
Note that we tested several FNN topologies with different
parameter settings. We only reported the best result of FNN in
this paper.
CONCLUSION
This paper has presented a systematic trajectory recognition
algorithm framework that can construct effective classifiers for
acceleration-based handwriting and gesture recognition. The
proposed trajectory recognition algorithm consists of acceleration
acquisition, signal preprocessing, feature generation, feature
selection, and feature extraction.With the reduced features,
a PNN can be quickly trained as an effective classifier. In the
experiments, we used 2-D handwriting digits and 3-D hand
gestures to validate the effectiveness of the proposed device
and algorithm. The overall handwritten digit recognition rate
was 98%, and the gesture recognition rate was also 98.75%.
This result encourages us to further investigate the possibility of
using our digital pen as an effective tool for HCI applications.
[attachment=52988]
Abstract
This paper presents an accelerometer-based digital
pen for handwritten digit and gesture trajectory recognition applications.
The digital pen consists of a triaxial accelerometer, a
microcontroller, and an RF wireless transmission module for sensing
and collecting accelerations of handwriting and gesture trajectories.
The proposed trajectory recognition algorithm composes of
the procedures of acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature extraction. The
algorithm is capable of translating time-series acceleration signals
into important feature vectors. Users can use the pen to write digits
or make hand gestures, and the accelerations of hand motions
measured by the accelerometer are wirelessly transmitted to a
computer for online trajectory recognition. The algorithm first
extracts the time- and frequency-domain features from the acceleration
signals and, then, further identifies the most important
features by a hybrid method: kernel-based class separability for
selecting significant features and linear discriminant analysis for
reducing the dimension of features.
INTRODUCTION
EXPLOSIVE growth of miniaturization technologies in
electronic circuits and components has greatly decreased
the dimension and weight of consumer electronic products,
such as smart phones and handheld computers, and thus made
them more handy and convenient. Due to the rapid development
of computer technology, human–computer interaction
(HCI) techniques [1]–[3] have become an indispensable component
in our daily life. Recently, an attractive alternative,
a portable device embedded with inertial sensors, has been
proposed to sense the activities of human and to capture
his/her motion trajectory information from accelerations for
recognizing gestures or handwriting.
RELATED WORK
Recently, some studies have focused on the development of
digital pens for trajectory recognition and HCI applications.
For instance, an alternative method of conventional tablet-based
handwriting recognition has been proposed by Milner [15]. In
his system, two dual-axis accelerometers are mounted on the
side of a pen to generate time-varying x- and y-axis acceleration
for handwriting motion. The author employed an HMM
with a bandpass filtering and a down-sampling procedure for
classification of seven handwritten words. The best recognition
rate is 96.2% when the number of states of the HMMis equal to
60. Oh et al. [16] presented a wandlike input device embedding
a triaxial accelerometer and a triaxial gyroscope for online 3-D
character gesture recognition. Fisher discriminant analysis was
adopted, and different combinations of sensor signals were used
to test the recognition performance of their device. When all six
axes raw signals were used as inputs of the recognition system,
the recognition rate was 93.23%.
TRAJECTORY RECOGNITION ALGORITHM
The block diagram of the proposed trajectory recognition
algorithm consisting of acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature
extraction is shown in Fig. 3.
In this paper, the motions for recognition include Arabic
numerals and eight hand gestures. The acceleration signals of
the hand motions are measured by a triaxial accelerometer and
then preprocessed by filtering and normalization. Consequently,
the features are extracted from the preprocessed data to represent
the characteristics of different motion signals, and the
feature selection process based on KBCS picks p features out of
the original 24 extracted features. To reduce the computational
load and increase the recognition accuracy of the classifier, we
utilize LDA to reduce the dimension of the selected features.
The reduced feature vectors are fed into a PNN classifier to
recognize the motion to which the feature vector belongs. We
now introduce the detailed procedure of the proposed trajectory
recognition algorithm as follows.
Signal Preprocessing
The raw acceleration signals of hand motions are generated
by the accelerometer and collected by the microcontroller.
Due to human nature, our hand always trembles slightly while
moving, which causes certain amount of noise. The signal
preprocessing consists of calibration, a moving average filter,
a high-pass filter, and normalization. First, the accelerations
are calibrated to remove drift errors and offsets from the raw
signals.
EXPERIMENTAL RESULTS
In this section, the effectiveness of trajectory recognition
algorithm is validated by the following two experiments:
1) handwritten digit recognition and 2) gesture recognition. The
proposed trajectory recognition algorithm consists of the following
procedures: acceleration acquisition, signal preprocessing,
feature generation, feature selection, and feature extraction.
We collected the acceleration signals of the two experiments
from ten subjects (four females, six males; age 24.1 ± 2.13
years old) in a laboratory environment. We used different
combinations of feature selection and extraction methods and
employed PNN to recognize handwritten digits and hand gestures.
In addition, we compared the recognition results of the
PNN trained by the features from different feature engineering
methods with those of feedforward neural networks (FNNs).
Note that we tested several FNN topologies with different
parameter settings. We only reported the best result of FNN in
this paper.
CONCLUSION
This paper has presented a systematic trajectory recognition
algorithm framework that can construct effective classifiers for
acceleration-based handwriting and gesture recognition. The
proposed trajectory recognition algorithm consists of acceleration
acquisition, signal preprocessing, feature generation, feature
selection, and feature extraction.With the reduced features,
a PNN can be quickly trained as an effective classifier. In the
experiments, we used 2-D handwriting digits and 3-D hand
gestures to validate the effectiveness of the proposed device
and algorithm. The overall handwritten digit recognition rate
was 98%, and the gesture recognition rate was also 98.75%.
This result encourages us to further investigate the possibility of
using our digital pen as an effective tool for HCI applications.