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1166 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 14, NO. 5, SEPTEMBER 2010
A Triaxial Accelerometer-Based Physical-Activity
Recognition via Augmented-Signal Features
and a Hierarchical Recognizer
Adil Mehmood Khan, Young-Koo Lee, Sungyoung Y. Lee, and Tae-Seong Kim, Member, IEEE
Abstract—Physical-activity recognition via wearable sensors can
provide valuable information regarding an individual’s degree of
functional ability and lifestyle. In this paper, we present an ac-
celerometer sensor-based approach for human-activity recogni-
tion. Our proposed recognition method uses a hierarchical scheme.
At the lower level, the state to which an activity belongs, i.e., static,
transition, or dynamic, is recognized by means of statistical signal
features and artificial-neural nets (ANNs). The upper level recog-
nition uses the autoregressive (AR) modeling of the acceleration
signals, thus, incorporating the derived AR-coefficients along with
the signal-magnitude area and tilt angle to form an augmented-
feature vector. The resulting feature vector is further processed
by the linear-discriminant analysis and ANNs to recognize a par-
ticular human activity. Our proposed activity-recognition method
recognizes three states and 15 activities with an average accuracy
of 97.9% using only a single triaxial accelerometer attached to the
subject’s chest.
Index Terms—Accelerometer, artificial-neural nets (ANNs), au-
toregressive (AR) modeling, human-activity recognition.
I. INTRODUCTION
AUTOMATIC recognition of human activities is one of the
important and challenging research areas in proactive and
ubiquitous computing: first, due to its potential in providing
personalized support for many different applications such as
smart environments and surveillance and second, due to its con-
nection to many different fields of studies such as lifecare and
healthcare.
Human-activity recognition requires an objective and reli-
able technique that can be used under the conditions of daily
living. Complex sensors such as cameras in computer vision
have been used to recognize activities. In general, the computer
vision-based techniques for tracking and activity recognition
often work well in a laboratory or well-controlled environment.
Manuscript received July 10, 2009; revised December 23, 2009 and
March 17, 2010; accepted May 20, 2010. Date of publication June 7,
2010; date of current version September 3, 2010. This research was sup-
ported by the Ministry of Knowledge Economy (MKE), Korea, under the
Information Technology Research Center (ITRC) support program super-
vised by the National IT Industry Promotion Agency (NIPA) (NIPA-2010-
(C1090-1021-0003 )).
A. M. Khan, Y.-K. Lee, and S. Y. Lee are with the Department of Com-
puter Engineering, Kyung Hee University, Yongin-si 446-701, Korea (e-mail:
kadil@oslab.khu.ac.kr; yklee@khu.ac.kr; sylee@oslab.khu.ac.kr).
T.-S. Kim is with the Department of Biomedical Engineering, Kyung Hee
University, Yongin-si 446-701, Korea (e-mail: tskim@khu.ac.kr).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TITB.2010.2051955
However, they fail in achieving the same level of accuracy un-
der a real-home setting due to the clutter, variable lighting, and
highly varied activities that take place in the natural environ-
ments [1]. Motion capture with the body-fixed accelerometers
offers an appropriate alternative for the assessment of daily
physical activities [2].
Many human-activity recognition systems have been devel-
oped in the past which incorporate the use of accelerometers.
Some of these investigated the use of acceleration signals to an-
alyze and classify different types of the same physical activity,
e.g., walking along the corridor, upstairs, and downstairs [3],
[4]. Others employed them for recognizing a wide set of daily
physical activities such as lying, sitting, standing, walking, and
running [2], [5]–[19]. Minnen et al. [20] have explored the dis-
covery of recurring activities such as hammering, sawing, fill-
ing, drilling, sanding, and grinding from the accelerometer data,
while [21]–[23] focused on the elderly persons’ fall detection
and prevention.
Most of these systems investigated the use of multiple ac-
celerometers attached to different sites on a subject’s body [2],
[3], [5], [8], [10], [11], [15], [17]–[21]. However, this approach
is not feasible for the long-term activity monitoring because
of two or more sensor-attachment sites and cable connections.
Comparatively, a small number of studies have investigated the
use of a single accelerometer mounted at waist or sternum [4],
[6], [7], [9], [12]–[14], [16], [22], [23]. Such systems achieved
good recognition results for some basic activities including ly-
ing, walking, and running. However, they could not achieve the
same level of accuracy for some static activities such as standing
and sitting, transitions such as lie-stand, sit-stand, and stand-sit,
and dynamic activities such as walking downstairs and walking
upstairs.
As for the features, most studies have used the frequency-
derived features employing fast Fourier transform [17], [20] and
wavelet transform [2]–[4]. Others used the signal-magnitude
area (SMA) [6], [7], [16], [24], tilt angle (TA) [6]–[8], [16] or
parameters such as averages, energy, entropy, standard devia-
tion, or correlations [10], [13], [18]. However, these features
are calculated over long time-windows which reduce their abil-
ity to detect the short-duration movements, e.g., the transitions
between sitting and standing or taking a couple of steps.
As for the recognition techniques, a large number of classifi-
cation methods have been investigated. Some studies incorpo-
rated the idea of simple heuristic classifiers [2], [5]–[9], [11],
[16], whereas others employed more generic and automatic
methods from the machine learning literature including the
1089-7771/$26.00 © 2010 IEEE
KHAN et al.: TRIAXIAL ACCELEROMETER-BASED PHYSICAL-ACTIVITY RECOGNITION 1167
decision trees, nearest neighbor and Bayesian networks [10],
[13], [17], [18], support vector machines [13], neural networks
[3], [15], [17], [21], Gaussian mixture models (GMM) [14], and
Markov chains [12], [19], [20].
Thus, the existing literature on physical-activity recognition
using accelerometers varies in approach, intention, and out-
come. Individual researchers have employed their own device(s)
to collect the data for a particular set of movement(s) and have
investigated a wide variety of algorithms and methods. The most
significant breakthrough is presented in [24], where a single tri-
axial accelerometer is developed and evaluated for accessing
daily physical activities. It was later used in [6], [7], and [16]
with varying success rates. However, the primary drawback of
these systems is their rule-based heuristic nature as finding such
a set of rules is a time-consuming process. Allen et al. [14] pro-
posed an improvement by employing a GMM-based approach as
a more general and sophisticated approach to physical-activity
recognition using a single triaxial accelerometer. However, this
scheme introduces more complexity because it requires training
a separate GMM for each physical activity. It also requires an
adaptation method to adapt the system to a particular subject
when faced with the limited training data. Moreover, all previ-
ous studies presented some difficulty in distinguishing between
the sitting and standing postures. Accuracy of 78% has been
achieved so far by relying on the improved knowledge of the
transitional movements between sitting and standing to distin-
guish these postures.
In our previous study on human-activity recognition using
a triaxial accelerometer [25], we proposed the autoregressive
(AR) modeling [26] of the triaxial acceleration signals and used
the AR-coefficients augmented with the SMA and TA to form
an augmented-feature vector. The proposed feature vector was
then used to recognize lying, standing, walking, and running.
It outperformed the features used in the previous studies by
achieving a recognition rate of 99%. However, the accuracy
decreased significantly with the addition of the new activities.
In this paper, we aim to overcome the limitations of our previ-
ous physical-activity recognition system and intend to develop a
system that is capable of recognizing a broad set of daily physi-
cal activities using only a single triaxial accelerometer. Our pro-
posed system uses a hierarchical-recognition scheme, i.e., the
state recognition at the lower level using statistical features and
the activity recognition at the upper level using the augmented-
feature vector [25] followed by the linear-discriminant analysis
(LDA) to extract only the meaningful features. Our proposed
system is capable of recognizing 15 physical activities of daily
living with a much improved recognition rate compared to the
previous studies.
II. METHOD
A. Sensor Device and Data Collection
We used an accelerometer sensor called Witilt v2.5, a
2.4-GHz wireless triaxial tilt sensor from Sparkfun. It employs
a FreeScale MMA7260Q triple-axis accelerometer and a Class
1 Bluetooth link from BlueRadios. The sampling frequency was
20 Hz and the range of the sensor output was ±6g. The blue-
Fig. 1. Sensor device being worn by a subject.
TAB LE I
CLASSIFIED STATES AND ACTIVITIES RECOGNIZED IN THIS STUDY
tooth module communicated with a universal serial bus dongle
attached to a computer working as a transceiver to receive and
store the sensor data. In general, the output of any body-worn
accelerometer depends on the position at which it is placed [27].
Accelerometers are normally attached to the part of the body
whose movement is being analyzed such as arm, wrist, thigh,
etc. However, since we wished to study the whole body move-
ments, we decided to place the sensor at a position closer to the
center of mass, i.e., the subject’s chest as shown in Fig. 1.
The dataset for our experiment was collected in an unsuper-
vised study. Six healthy subjects, i.e., three females and three
males with the mean age of 27, wore the device each day for a
period of one month to collect the 15 activities which are listed in
Table I. Annotations were performed using a bluetooth headset
combined with a speech-recognition software [28]. A sample se-
quence of the activities performed at home is: sitting (2 min) →
sit-stand →standing (2 min) →stand-lie →lying (2 min) →
lie-stand →standing (40 s) →walking (2 min) →standing
(40 s) →walking-upstairs →standing (40 s) →walking-
downstairs →standing (40 s) →stand-sit →sitting (40 s) →
sit-lie →lying (40 s) →lie-sit.
The subjects were provided with approximate time duration
for each activity, as shown in the activity sequence, except for
the walking upstairs and downstairs. The time duration of these
activities depended on the length of stairs at each subject’s home
and, thus, varied among the subjects. Although the dataset col-
lected under this protocol was structured, it was still acquired
1168 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 14, NO. 5, SEPTEMBER 2010
Fig. 2. Set of the sample acceleration signals of the human activities for each axis of the triaxial accelerometer.
under the less controlled conditions than in the most prior
studies.
The subjects were trained on the use of data collection and
annotation applications. Each subject then collected the activity
data at home without the researchers’ supervision. They made
the annotations themselves throughout the data collection. We
collected approximately 21 h of the activity data, i.e., 3.5 h per
subject. The activity dataset for each subject was then divided
randomly into the training and test sets in a roughly 40%–60%
split. A representative set of the activity signals is shown in
Fig. 2.
B. Proposed System Architecture
The architecture of our proposed activity-recognition system
is illustrated in Fig. 3. A component-based description of the
system is given in the following.
1) Noise Reduction: The real-time output of an accelerome-
ter contains some noise that needs to be filtered out before using
it for the activity recognition. The noise reduction unit, shown in
Fig. 3, incorporates a three-point moving average filter to filter
out the signal outliers.
2) State Recognition: The purpose of the state recognition
is to determine the state to which an activity belongs. Features,
including the mean, standard deviation, spectral entropy, and
correlation, as the state features, are extracted from the noise-
reduced acceleration signal and processed by a classifier. These
parameters have been used for physical-activity recognition in
Fig. 3. Block diagram for our proposed recognition technique.
several existing studies with varying success rates [10], [13],
[18]. However, we proposed their usage for the state recognition
only. The mean, range of the possible acceleration values, and
periodicity in the acceleration data differ slightly between the
physical activities but greatly between the states. Hence, they
are more suitable for the state recognition.
3) Activity Recognition: Once the state of a given activ-
ity is recognized, the noise-reduced acceleration signal is fed
to the activity-recognition module which uses our proposed
augmented-feature vector [25], i.e., the AR-coefficients, SMA,
and TA. Though the TA has been used previously to differentiate
certain postures [6]–[8], [16], the decisions made on its values
were purely heuristic and rule based. We proposed using the TA
KHAN et al.: TRIAXIAL ACCELEROMETER-BASED PHYSICAL-ACTIVITY RECOGNITION 1169
Fig. 4. Block diagram for our activity-recognition method, showing the com-
ponents of the augmented-feature vector.
as a part of an augmented-feature vector. The block diagram
of this module is shown in Fig. 4. A brief description of each
feature is given as follows.
1) Autoregressive coefficients: An AR model can be repre-
sented as
y(t)=
p
i=1
α(i)y(t−i)+ε(t)(1)
where α(i)is the AR-coefficients, y(t)the time series
under investigation which in our case is the acceleration
signal, pthe order of the filter, and ε(t)the noise term.
2) Signal-magnitude area (SMA): It is calculated according
to
SMA =
N
i=1
(|x(i)|)+(|y(i)|)+(|z(i)|)(2)
where x(i),y(i), and z(i)indicate the acceleration signal
along the x-axis, y-axis, and z-axis, respectively.
3) Tilt angle (TA): It refers to the relative tilt of the body in
space. It is defined as the angle between the positive z-axis
and the gravitational vector gand is calculated according
to
ϑ=arcos(z).(3)
C. Linear-Discriminant Analysis (LDA)
The LDA module takes the augmented-feature vector as input
and utilizes the class specific information to maximize the ratio
of the between and within-class scatter information. It seeks the
vectors in the underlying space to create the best discrimination
among different classes. It is well known for the feature ex-
traction and dimension reduction. The optimal discrimination
projection matrix Dopt is chosen from the maximization of the
ratio of the determinant of the between and within-class scatter
matrices as
Dopt = arg max
D
DTSBD
|DTSWD|=[d1,d
2,...,d
t]T(4)
where SBand SWare the between and within-class scatter
matrices, respectively. Further details on the LDA are available
in [29]. The 3-D feature plots for the four transitions before and
after applying the LDA are shown in Figs. 5 and 6, respectively.
Fig. 5. 3-D feature plot for the four transitions before the LDA.
Fig. 6. 3-D feature plot for the four transitions after the LDA.
D. Classifier
It is the fundamental element of the system which should be
adaptive and capable of providing the correct classification. In
other words, it must correctly understand the features even if
those features are considerably different from the ones it was
fed before. For this reason, we decided to use artificial-neural
nets (ANNs) based on the feed-forward backpropagation algo-
rithm. Perceptron neural nets with different number of layers
and neurons were tested in order to optimize the performance.
The maximal value of the weights in the neuron connections was
normalized to the modulus of 1. Different steps of the increment
for the weights were also investigated. The training of ANNs
was also repeated several times by changing the input order in
a random fashion.
For the state recognition, only one network (SNN) was
trained. The inputs to SNN were the state features. For the activ-
ity recognition, three networks were trained for three states, i.e.,
an ANN to recognize the static activities, an ANN to recognize
the transitions, and an ANN to recognize the dynamic activities.
1170 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 14, NO. 5, SEPTEMBER 2010
The inputs to each of these ANNs were the output of the LDA
module as shown in Fig. 4.
III. EXPERIMENTAL RESULTS
A. Determination of the AR-Model Order
In order to determine the optimal AR-model order, root mean
square error (rmse) values were calculated for each activity and
the behavior of the rmse curve against different model orders
was analyzed. We observed a decreasing trend against the AR-
model order. The curve tended to even out near the model order
of 10 which suggested that the optimum model order lied in the
neighboring values of 10.
B. Classification Results
In order to get the meaningful coefficients, the AR-modeling
of any time series generally requires the length of the series to
be significantly larger than the model order. We used a sam-
pling frequency of 20 Hz. It resulted in the failure of seizing the
meaningful AR-coefficients on the second-by-second basis, i.e.,
a window size of 20 samples. After several trials, we concluded
that the window size of 64, i.e., 3.2 s, with no overlapping be-
tween consecutive windows, is the most appropriate in our case.
First, this time interval was not too long to result in a delayed
response. Second, it provided enough raw data for extracting the
meaningful AR-coefficients. Thus, for the classification, accel-
eration data was fed every 3.2 s to the recognition system and
the output was compared to the true activity. The performance
of the proposed-recognition system was then validated in the
following four studies.
1) Test on State Recognition: The state features were ex-
tracted and the SNN was trained to recognize the three states.
The average-recognition accuracy was almost 99% which indi-
cates the feasibility of employing the proposed features for the
state recognition.
2) Test on Feature Augmentation: This study was performed
in order to try a number of configurations of the front-end
features for the activity recognition before landing on the
augmented-feature vector which is shown in Fig. 4. We per-
formed three different trials. Each trial involved a differ-
ent combination of the features for classifying the test ac-
tivities, i.e., {AR-coefficients},{AR-coefficients, SMA}, and
{AR-coefficients, SMA, and TA}. The recognition system used
in this study was single-level (SL-HAR), i.e., the features were
calculated and fed directly to the activity-recognition module
without the state recognition and a single ANN was trained to
recognize only the four basic activities, including lying, stand-
ing, walking, and running. The recognition results are summa-
rized in Table II.
3) Single-Level (SL) Versus Hierarchical (H)-HAR System:
The purpose of this study was to evaluate the performance of
the SL-HAR versus H-HAR system to recognize the 15 activ-
ities which are listed in Table I. This study was performed in
two steps. First, the state features and augmented-feature set
were calculated and fed to the SL-HAR system to recognize
the test activities. Second, the proposed H-HAR system was
TAB LE I I
AVERAGE-RECOGNITION RESULTS(IN PERCENT)FOR THE FOUR ACTIVITIES FOR
THE FEATURE-AUGMENTATION TESTS
TABLE III
AVERAGE RECOGNITION RESULTS(IN PERCENT)FOR ALL ACTIVITIES FOR THE
SL-HAR VERSUS H-HAR TEST
used to recognize the test activities. The recognition results are
summarized in Table III. They clearly indicate that the H-HAR
system outperformed the SL-HAR system by achieving an av-
erage recognition accuracy of 97.9%. The choice of 15 physical
activities, belonging to three states, generated complex deci-
sion boundaries in the feature space. The two-level recognition
broke down the overall recognition problem into two subprob-
lems and, thus, enabled the H-HAR to incorporate a set of two
ANNs to solve these complex decision boundaries.
4) Performance Evaluation With Limited Training Data:
Accelerometer-based human-activity recognition systems usu-
ally suffer a loss in accuracy during the practical deployment,
which is caused by either training the system with the data from
other people or with the limited training data from the person
for whom it is intended. To address this problem, we estimated
the subject independent classifier performance of the H-HAR
system using the sixfold cross validation, where six represents
the number of subjects participated in our study. Of these six
subjects, the activity dataset from a single subject was retained
as the validation dataset for testing the model and the activ-
ity datasets from the remaining five subjects were used as the
training datasets. This process was repeated six times, i.e., the
number of folds. The results from these folds are summarized
in Table IV. The average recognition accuracy of 97.65% indi-
cates that our proposed human-activity recognition scheme can
achieve high recognition rates for a specific subject even if an
adequate amount of training data from the intended subject is
unavailable.
KHAN et al.: TRIAXIAL ACCELEROMETER-BASED PHYSICAL-ACTIVITY RECOGNITION 1171
TAB LE I V
AVERAGE RECOGNITION RESULTS FOR ALL ACTIVITIES WHEN THE TRAINING
DATA FROM THE INTENDED SUBJECT IS NOT AVAI L A B L E
IV. DISCUSSION AND CONCLUSION
The aim of this paper is to provide an accurate and robust
human-activity recognition system for the u-lifecare environ-
ments. The proposed system incorporates the use of a single
triaxial accelerometer. It is feasible to be used by the free-living
subjects as it relies only on a single point of sensor’s attach-
ment to their bodies. It is effective in a sense that it is capable
of recognizing a broad set of daily physical activities with an
average accuracy of 97.9%. It is able to distinguish between the
sitting and standing postures, sit-stand and stand-sit transitions,
and walking-upstairs and walking-downstairs movements using
only a single triaxial accelerometer.
Although several systems have been proposed in the past
to monitor daily physical activities using accelerometers, this
system appears promising in several regards. First, its perfor-
mance compares favorably with the previously proposed sys-
tems. Mathie et al. proposed a rule-based heuristic system for
the classification of daily physical activities. However, the pri-
mary drawback of this system is its rule-based heuristic nature
as finding such a set of rules is a time-consuming process. It
also showed difficulty in distinguishing between the sit-stand
and stand-sit transitions and between the sitting and standing
postures [6], [16]. Allen et al. proposed an improvement by em-
ploying a GMM-based approach but it also exhibited difficulty
in distinguishing between sitting and standing postures [14].
Bao et al. used an ambulatory system based on the decision
trees to classify 20 activities using a seminaturalistic dataset
collected outside the laboratory [10]. However, the recognition
accuracy was only 84.26%. It also required subjects to wear five
biaxial accelerometers simultaneously on different parts of their
bodies and, thus, may not be feasible for the long-term activity
recognition in the free-living subjects. Ermes et al. proposed a
system based on a hybrid model classifier to assess the feasibil-
ity of the activity recognition in out-of-lab settings using both
the supervised and unsupervised activity data [17]. However,
the overall accuracy was 89% and the system showed inability
in differentiating between the sitting and standing postures.
Second, the dataset used in this study was collected by the
subjects at home without the researchers’ supervision and the
annotations were made on the spot. The use of a bluetooth
headset together with a speech-recognition software for the an-
notations resulted in very little interference while performing
the activities. Third, our proposed system is based on a novel
hierarchical recognition scheme. Our results show that it is su-
perior to a single-level recognition system as a large number of
activities result in complex decision boundaries in the feature
space which are difficult for a single classifier to solve.
Fourth, a novel augmented-feature vector was employed
to achieve the activity recognition within each state. It was
composed of the AR-coefficients, SMA, and TA. The AR-
coefficients were obtained by modeling the triaxial accelera-
tion signals using the AR-models. The recognition accuracy
of 97.9% illustrates the success of employing the proposed
augmented-feature vector for the activity recognition which in-
directly signifies the feasibility of using the AR-analysis. Fifth,
our results illustrate the success of our proposed recognition
system in achieving an acceptable recognition rate even in the
absence of an adequate amount of the training data from the
intended subject.
Thus, the experimental results of our proposed hierarchical
recognition scheme have shown significant potential in its ability
to accurately and robustly model the ambulatory data using a
single triaxial accelerometer. The next stage of evaluation will
be the assessment of our classification technique for the elderly
in a free-living context.
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Adil Mehmood Khan received the B.S. degree in
information technology from the National University
of Sciences and Technology (NUST), Rawalpindi,
Pakistan, in 2005. He is currently working toward the
Ph.D. degree in the Department of Computer Engi-
neering, Kyung Hee University, Yongin-si, Korea.
His current research interests include ubiquitous
computing, context-aware computing for handheld
devices, wearable sensing, and pattern recognition.
Young-Koo Lee, photograph and biography not available at the time of
publication.
Sungyoung Y. Lee, photograph and biography not available at the time of
publication.
Tae-Seong Kim, photograph and biography not available at the time of
publication.