Feature Selection Evaluation for Light Human Motion Identification
in Frailty Monitoring System
Evangelia Pippa
1
, Iosif Mporas
1,2
and Vasileios Megalooikonomou
1
1
Multidimentional Data Analysis and Knowledge Discovery Laboratory,
Dept. of Computer Engineering and Informatics, University of Patras, Rion-Patras, Greece
2
School of Engineering and Technology, University of Hertfordshire, Hatfield, U.K.
Keywords: Human Motion Detection, Machine Learning, Feature Extraction.
Abstract: In order to plan and deliver health care in a world with increasing number of older people, human motion
monitoring is a must in their surveillance, since the related information is crucial for understanding their
physical status. In this article, we focus on the physiological function and motor performance thus we
present a light human motion identification scheme together with preliminary evaluation results, which will
be further exploited within the FrailSafe Project. For this purpose, a large number of time and frequency
domain features extracted from the sensor signals (accelerometer and gyroscope) and concatenated to a
single feature vector are evaluated in a subject dependent cross-validation setting using SVMs. The mean
classification accuracy reaches 96%. In a further step, feature ranking and selection is preformed prior to
subject independent classification using the ReliefF ranking algorithm. The classification model using
feature subsets of different size is evaluated in order to reveal the best dimensionality of the feature vector.
The achieved accuracy is 97% which is a slight improvement compared to previous approaches evaluated
on the same dataset. However, such an improvement can be considered significant given the fact that it is
achieved with lighter processing using a smaller number of features.
1 INTRODUCTION
The ageing population around the world is
increasing and it is estimated that two billion people
will be aged over 65 years by 2050. This will affect
the planning and delivery of health and social care as
well as the clinical condition of frailty. Frailty is a
medical syndrome which is characterized by
diminished strength, endurance, and reduced
physiologic function that increases an individual’s
vulnerability for developing increased dependency
and/or death (Morley et al., 2013). Frailty is
characterized by multiple pathologies such as weight
loss, weakness, low activity, slow motor
performance, balance and gait abnormalities, as well
as cognitive ones (Chen et al., 2014). Frailty
increases risks of incident falls, worsening of
mobility, disability, hospitalization or
institutionalization, and mortality (Abellan et al. ,
2008; Mitnitski et al., 2002; Morley et al., 2006),
which in turn increase the burden to cares and costs
to the society.
It is assumed that early intervention with frail
persons will improve quality of life and reduce
health services costs. Thus it is essential to develop
real life tools for the assessment of physiologic
reserve and the need to test interventions that alter
the natural course of frailty since frailty is a dynamic
and not an irreversible process. Several efforts have
been done in this direction through research and
development activities. In (Seacw Project) an
Ecosystem for training, informing and providing
tools, processes, methodologies for ICT and active,
healthy aging was developed mainly targeting to
caregivers, older people and general population. In
(Eldergames Project) an interactive tabletop
platform able to integrate potentialities derived from
both technology and leisure activities was designed.
Another purpose of (Eldergames Project) was the
monitoring of the older people status, with
information about his/her progression /regression in
cognitive health. In (Kinoptim Project) a home-care
solution that will address older people living in the
community in a preventive manner and rely on ICT
and virtual reality gaming through the exploitation
of haptic technologies, vision control and context
88
Pippa, E., Mporas, I. and Megalooikonomou, V.
Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System.
In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), pages 88-95
ISBN: 978-989-758-180-9
Copyright
c
2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Figure 1: The FrailSafe conceptual diagram for motion monitoring.
awareness methods was developed and integrated,
while promising to redefine fall prevention by
motivating people to be more active, in a friendly
way and with tele-supervision if necessary. In
(Mporas et al., 2015) a more holistic, personalized,
medically efficient and economical monitoring
system for people with epilepsy was provided. In
(Doremi Project) multidisciplinary research areas in
serious games, social networking, Wireless Sensor
Network, activity recognition and contextualization,
behavioral pattern analysis were combined in pilot
setups involving both older users and care providers.
In (Alfred Project) a mobile, personalized assistant
for older people was developed, using cutting edge
technologies such as advanced speech interaction,
which helps them stay independent, coordinate with
carers and foster their social contacts. In (Home
Sweet Home Project) new, economically sustainable
home assistance service which extends older people
independent living was introduced, measuring the
impact of monitoring, cognitive training and e-
Inclusion services on the quality of life of older
people, on the cost of social and healthcare delivered
to them, and on a number of social indicators. In
(Mobiserv Project) the objective was to develop and
test a proactive personal robotic, integrated with
innovative sensors, localization and communication
technologies, and smart textiles to support
independent living for older adults, in their home or
in various degrees of institutionalization, with a
focus on health, nutrition, well-being, and safety. In
(Fate Project) innovative ICT-based solution for the
detection of falls in ageing people were studied,
covering prevention and detection of falls in all
circumstances.
Human motion monitoring is a must in
surveillance of older people, since the related
information is crucial for understanding the physical
status and the behavior of the older people. This is
typically achieved using image/video and
accelerometer based data (Foroughi et al., 2008;
Xiang et al., 2015;Yang et al., 2010). In this article
we focus on the physiological function and motor
performance thus we present a light human motion
identification scheme together with preliminary
evaluation results, which will be further exploited
within the FrailSafe (Frailsafe Project) architecture.
The main contributions of this paper are summarized
in the following:
the classification accuracy in the examined
dataset is slightly improved (about one unit)
compared to previous approaches.
a lighter human motion identification module
using less features but achieving equal
accuracy in comparison to the one previously
proposed for the dataset under consideration is
achieved by feature selection
The reminder of this article is organized as follows.
In Section 2 we present the FrailSafe concept. In
Section 3 we describe the human motion
identification scheme. In Sections 4 and 5 we
present the experimental setup and the evaluation
results respectively. Finally, in Section 6 we
conclude this work.
2 THE FRAILSAFE CONCEPT
FrailSafe aims to better understand frailty and its
relation to co-morbidities, to develop quantitative
and qualitative measures to define frailty and to use
these measures to predict short and long-term
outcome. In order to achieve these goals real life
tools for the assessment of physiological reserve and
Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System
89
Figure 2: Human Motion Identification Module.
of external challenges will be developed. These tools
will provide an adaptive model (sensitive to
changes) in order that pharmaceutical and non-
pharmaceutical interventions, which will be
designed to delay, arrest or even reverse the
transition to frailty. Moreover, FrailSafe targets at
creating "prevent-frailty" evidence based
recommendations for older people regarding
activities of daily living, lifestyle and nutrition, as
well as strengthening the motor cognitive, and
other"anti-frailty" activities through the delivery of
personalized treatment programs, monitoring alerts,
guidance and education. The FrailSafe conceptual
infrastructure for motion monitoring is illustrated in
Figure 1.
Through patient-specific interventions, FrailSafe
aims to define a frailty measure. This measure is
initially constructed from prior knowledge on the
field, and then globally updated based on analysis of
long-term observations of all older peoples' states.
This update is then applied to the individual patient
models, modifying them accordingly, to fit different
needs per patient. The monitoring of the older
people's motion activity is performed through the
environmental sensors module, which includes
accelerometer sensors for the monitoring of the
human motions. Details about the light motion
identification implementation are provided in the
next section.
3 LIGHT HUMAN MOTION
IDENTIFICATION
The presented workflow for light human motion
identification is part of an end-to-end system for
sensing and predicting treatment of frailty and
associated co-morbidities using advanced
personalized models and advanced interventions, the
FRAILSAFE framework.
The proposed classification methodology can be
used as a core module in order to discriminate the
detected motions to six basic activities: walking,
walking-upstairs, walking-downstairs, sitting,
standing and laying. The block diagram of the
overall workflow for learning the activity classifiers
is illustrated in Figure 2.
The multi-parametric sensor (accelerometer and
gyroscope) data are pre-processed as in (Anguita et
al., 2012; Reyes-Ortiz et al., 2013) by applying noise
filters and then sampled in fixed-width sliding
windows
,1 (frames) of 2.56 sec and
50% overlap. The sensor acceleration signal, which
has gravitational and body motion components, was
separated using a Butterworth low-pass filter into
body acceleration and gravity. The gravitational
force is assumed to have only low frequency
components, therefore a filter with 0.3 Hz cut-off
frequency was used. From each frame, a vector of
features
∈
,
|
|
|

| was obtained by
calculating variables from the time
∈
|
|
and
frequency domain

∈
|
|
.
The extracted time domain and frequency
domain features are concatenated to a single feature
vector as a representative signature for each frame.
Details on the type of extracted features are provided
in Section 4.
All frames are used as input to
FRAILSAFE's human motion identification module
which classifies basic activities of daily living
(ADLs) in order to obtain some preliminary
evaluation results for the proposed architecture. In
this module, a model for multiclass classification
between six basic ADLs (walking, walking-upstairs,
walking-downstairs, sitting, standing and laying),
which has been previously built in a training phase,
is used in order to label the frames. Each frame is
ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
90
classified independently.
During the training phase of the classification
architecture, frames with known class labels (labeled
manually) are used to train the multiclass
classification model. During the test phase the
unknown multi-parametric sensor signals are pre-
processed and parameterized with similar setup as in
the training phase. Each extracted feature vector is
provided as input to the trained classifier.
4 EXPERIMENTAL SETUP
4.1 Data
The previously described classification methodology
was evaluated on multi-parametric data from the
UCI HAR Dataset (Anguita et al., 2013). The dataset
consists of accelerometer and gyroscope recordings
from 30 volunteers within an age bracket of 19-48
years when performing six activities (walking,
walking-upstairs, walking-downstairs sitting,
standing, laying). For the experiments each person
worn a smartphone (Samsung Galaxy S II) on the
waist. Using its embedded accelerometer and
gyroscope, 3-axial linear acceleration and 3-axial
angular velocity at a constant rate of 50Hz were
captured. The data were labelled manually using the
corresponding video recordings which were captured
during the experiments. Since the evaluation here
was held using a subject dependent cross-validation
setting, data were initially merged in a single dataset
and then split in 30 datasets, one for each subject.
4.2 Feature Extraction and
Classification Algorithm
Initially, the sensor signals (accelerometer and
gyroscope) were pre-processed as proposed in
(Anguita et al., 2012, Reyes-Ortiz et al., 2013) in
order to proceed with feature extraction. The
features selected for this analysis are those proposed
in (Anguita et al., 2012, Reyes-Ortiz et al., 2013)
which come from the accelerometer and gyroscope
3-axial raw signals denoted as tAcc-XYZ and tGyro-
XYZ with prefix 't' used to denote time. The
sampling frequency of these time domain signals
was 50 Hz. In order to remove noise Anguita et al.
performed low pass filtering using a median filter
and a 3rd order low pass Butterworth filter with a
corner frequency of 20 Hz. Then, in order to
separate the acceleration signal into body and
gravity acceleration signals denoted as tBodyAcc-
XYZ and tGravityAcc-XYZ, they used another low
pass Butterworth filter with a corner frequency of
0.3 Hz.
Subsequently, Jerk signals denoted as
tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ were
obtained by the time derivation of the body linear
acceleration and angular velocity. Also, they used
the Euclidean norm to calculate the magnitude of
these three-dimensional signals yielding the
following signals: tBodyAccMag, tGravityAccMag,
tBodyAccJerkMag, tBodyGyroMag and
tBodyGyroJerkMag.
Finally a Fast Fourier Transform (FFT) was
applied to signals tBodyAcc-XYZ, tBodyAccJerk-
XYZ, tBodyGyro-XYZ, tBodyAccJerkMag,
tBodyGyroMag, tBodyGyroJerkMag producing
fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-
XYZ, fBodyAccJerkMag, fBodyGyroMag,
fBodyGyroJerkMag. Here, the prefix 'f' were used to
indicate frequency domain signals.
These signals were used to estimate variables of
the feature vector for each pattern: '-XYZ' is used to
denote 3-axial signals in the X, Y and Z directions.
The aforementioned signals which were produced by
processing accordingly the initial sensor recordings
are tabulated in Table 1.
The set of features that were extracted from these
signals are those proposed by Anguita et al. (Anguita
et al., 2012) including the mean value, the standard
deviation, the median absolute deviation, the largest
value in array, the smallest value in array, the signal
magnitude area, the energy measure as the sum of
the squares divided by the number of values, the
interquartile range, the signal entropy, the
autoregression coefficients with Burg order equal to
4, the correlation coefficient between two signals,
the index of the frequency component with largest
Table 1: Pre-processed Signals.
Signals
tBodyAcc-XYZ
tGravityAcc-XYZ
tBodyAccJerk-XYZ
tBodyGyro-XYZ
tBodyGyroJerk-XYZ
tBodyAccMag
tGravityAccMag
tBodyAccJerkMag
tBodyGyroMag
tBodyGyroJerkMag
fBodyAcc-XYZ
fBodyAccJerk-XYZ
fBodyGyro-XYZ
fBodyAccMag
fBodyAccJerkMag
fBodyGyroMag
fBodyGyroJerkMag
Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System
91
Table 2: Additional Signals.
Additional Siganls
gravityMean
tBodyAccMean
tBodyAccJerkMean
tBodyGyroMean
tBodyGyroJerkMean
magnitude, the weighted average of the frequency
components to obtain a mean frequency, the
skewness of the frequency domain signal, the
kurtosis of the frequency domain signal, the energy
of a frequency interval within the 64 bins of the FFT
of each window and the angle between two vectors.
Additional vectors were obtained by averaging
the signals in a signal window sample. These are
used on the angle variable (Table 2).
In conclusion, for each record a 561- feature
vector with the aforementioned time and frequency
domain variables was provided.
The computed feature vectors were used to train
a classification model. In order to evaluate the
ability of the above features to discriminate between
ADLs we examined the SMO (Keerthi et al., 2001;
Platt et al., 1998) with RBF kernel classification
algorithm, which was implemented by the WEKA
machine learning toolkit (Hall et al. 2009). SMO
algorithm is an implementation of Support Vector
Machines provided by the WEKA toolkit. Here we
selected SMO for the classification since SVMs are
used mostly in relevant literature.
During the test phase, the sensor signals were
pre-processed and parameterized as during training.
The SMO classification model was used to label
each of the activities. Evaluation was performed in a
subject dependent cross-validation setting.
In a further step we examined the discriminative
ability of the extracted features for the human
motion identification. The ReliefF algorithm
(Kononenko, 1994) (which is an extension of an
earlier algorithm called Relief (Kira and Rendell,
1992)) was used for estimating the importance of
each feature in multiclass classification. In the
ReliefF algorithm the weight of any given feature
decreases if the squared Euclidean distance of that
feature to nearby instances of the same class is more
than the distance to nearby instances of the other
class. ReliefF is considered one of the most
successful feature ranking algorithms due to its
simplicity and effectiveness (Dietterich, 1997; Sun
and Li, 2006; Sun and Wu, 2008) (only linear time
in the number of given features and training samples
is required), noise tolerance and robustness in
detecting relevant features effectively, even when
these features are highly dependent on other features
(Dietterich, 1997; Kononenko,1997).
Table 3: Subject Dependent Human Motion Identification
Accuracy.
Subject Accuracy
1 100%
2 93,71%
3 97,36%
4 93,69%
5 86,09%
6 92,92%
7 93,83%
8 98,93%
9 85,76%
10 95,92%
11 98,42%
12 97,50%
13 95,41%
14 95,67%
15 96,65%
16 94,81%
17 90,76%
18 96,70%
17 98,33%
20 96,33%
21 98,53%
22 99,38%
23 96,77%
24 98,95%
25 94,87%
26 96,94%
27 99,73%
28 93,72%
29 100,00%
30 97,65%
Table 4: Mean across subjects confusion matrix. Rows represent the actual class and columns the predicted class.
Standing Sitting Laying Walking Downstairs Upstairs
Standing 1795 110 0 0 0 1
Sitting 289 1485 2 0 0 1
Laying 0 0 1944 0 0 0
Walking 0 0 0 1718 2 2
Downstairs 0 0 0 5 1397 4
Upstairs 0 0 0 0 0 1544
ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
92
Table 5: ReliefF Feature Ranking.
Ranking Feauture
1 tGravityAcc_energy_X
2 fBodyAccJerk_entropy_X
3 fBodyAcc_entropy_X
4 fBodyAccJerk_entropy_Y
5 tBodyAccJerkMag_entropy
6 angle(X_gravityMean)
7 tGravityAcc_min_X
8 tGravityAcc_mean_X
9 tBodyAccJerk_entropy_X
10 tGravityAcc_max_X
11 fBodyBodyAccJerkMag_entropy
12 tBodyAcc_max_X
13 tBodyAccJerk_entropy_Y
14 fBodyAccMag_entropy
15 fBodyAcc_entropy_Y
16 fBodyAccJerk_entropy_Z
17 tBodyAccJerk_entropy_Z
18 tBodyGyroJerkMag_entropy
17 tGravityAcc_energy_Y
20 tBodyAccMag_entropy
21 tGravityAccMag_entropy
22 tGravityAcc_mean_Y
23 tBodyGyroJerk_entropy_Z
24 tGravityAcc_max_Y
25 fBodyAcc_entropy_Z
26 tGravityAcc_entropy_Z
27 tGravityAcc_min_Y
28 fBodyGyro_entropy_X
29 fBodyGyro_entropy_X
30 tBodyGyroJerk_entropy_X
31 fBodyAcc_mad_X
32 fBodyAcc_std_X
33 tBodyAcc_std_Y
34 fBodyAcc_mad_Y
35 tBodyAcc_std_X
36 fBodyBodyGyroJerkMag_entropy
37 tBodyAcc_mad_Y
38 fBodyGyro_entropy_Y
39 tBodyGyroMag_entropy
40 fBodyAcc_std_Y
Furthermore, ReliefF avoids any exhaustive or
heuristic search compared with conventional
wrapper methods and usually performs better
compared to filter methods due to the performance
feedback of a nonlinearclassifier when searching for
useful features (Sun and Wu, 2008).
In this study, ranking is performed on the whole
dataset including all frames from all subjects. We
examined the performance of the method, in terms
of accuracy for different number of N-best features
(N =10, 20, 30, ... 560 ), with respect to the ReliefF
feature ranking algorithm.
5 EXPERIMENTAL RESULTS
The classification methodology presented in Section
3 was evaluated using the classification algorithm
and the cross-validation scheme described in Section
4. The classification performance was evaluated in
terms of accuracy



(1)
where TP denotes the true positives, TN the true
negatives, FP the false positives and FN the false
negatives. The results of the method using all
features are shown on Table 3.
As can be seen in Table 3, the overall highest
accuracy of the proposed methodology for human
motion identification is 100% for subjects 1 and 29
and the lowest accuracy 85.76% was obtained for
the 9th subject. However, the mean accuracy is
relatively high 95,84%. The mean across all
subjects confusion matrix is shown in Table 4. As
can be seen all ADLs except sitting and standing are
nearly perfectly discriminated from the others with
only a few false dismissals or false alarms (2 to 5).
The misclassification of some sitting and standing
instances are probably owed to the similarity of
these ADLs.
In a further step, we applied feature ranking on
the whole dataset (consisting of all available
subjects) using the ReliefF algorithm as described in
Section 4. The performance of the classification, in
terms of accuracy, for different number of N-best
features (N =10, 20, 30, ..., 560) for the SMO
algorithm is shown in Figure 3.
As can be seen in Figure 3 the highest
classification accuracy is achieved when a large
subset of discriminative features are used.
Specifically, the highest accuracy is achieved for a
subset of 550 best features with a percentage of 97%
which is equal to the accuracy achieved when all
features are used. It seems that the size and the
variability of the dataset is relatively large requiring
a feature vector of high dimensionality to accurately
discriminate between the six classes. However, with
only 40 features a high accuracy equal to 90% can
be achieved.
Table 5 shows the 40 best features according to
the ReliefF ranking algorithm. Although it is best to
use a high dimensional feature vector to achieve
higher classification accuracy, feature selection is
still important in cases where a light human motion
identification module is needed such as in
FRAILSAFE.
Although direct comparison with other studies
Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System
93
Figure 3: Classification Accuracy for different subsets of N-best features (N=10,20,..., 550).
performed on the same dataset is not feasible due to
different problem identification (subject dependent
classification studied here versus subject
independent classification studied in previous
works) and different validation protocols followed
as well (cross validation used here instead of 70%
and 30% train and test sets respectively in the
literature), the proposed method for the subject
independent classification slightly improves the
classification accuracy from 94% and 96% achieved
in (Reiss et al., 2013) and (Romera-Paredes et al.,
2013, Kastner et al., 2013) respectively to 97%. This
improvement is significant since it is being achieved
with less features providing the means for lighter
approaches for human motion identification.
6 CONCLUSIONS
In this paper, we investigated the problem of human
motion identification from multi-parametric sensor
data acquired from accelerometers and gyroscopes
using a large number of time-domain and frequency
domain features in order to be used as part of an
end-to-end system for sensing and predicting
treatment of frailty and associated co-morbidities
using advanced personalized models and advanced
interventions. The proposed methodology was
evaluated in multi-parametric data from 30 subjects.
The evaluation of the multiclass SMO classification
algorithm showed that a mean accuracy of 96% was
achieved. Feature ranking investigation and
evaluation of the classification models using subsets
of features were performed and revealed the most
significant features for the classification task. The
use of the most discriminative features (N = 550)
achieved accuracy equal to the accuracy achieved
when all features are used.
ACKNOWLEDGEMENTS
The research reported in the present paper was
partially supported by the FrailSafe Project (H2020-
PHC-21-2015 - 690140) “Sensing and predictive
treatment of frailty and associated co-morbidities
using advanced personalized models and advanced
interventions”, co-funded by the European
Commission under the Horizon 2020 research and
innovation programme.
REFERENCES
Abellan van Kan, G., et al., 2008. The I.A.N.A Task Force
on frailty assessment of older people in clinical
practice. J Nutr Health Aging, 12(1): p. 29-37.
Alfred Project:
http://cordis.europa.eu/project/rcn/110629_en.html.
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz,
J.L., 2013. A Public Domain Dataset for Human
Activity Recognition Using Smartphones. ESANN
2013, 21th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine
Learning.
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz,
J.L., 2013. Human Activity Recognition on
Smartphones using a Multiclass Hardware-Friendly
Support Vector Machine. IWAAL 2012 International
Workshop of Ambient Assisted Living.
Chen, X., Mao, G., Leng, S.X., 2014. Frailty syndrome: an
overview. Clin Interv Aging, 9: p. 433-41.
Dietterich, T. G., 1997. Machine learning research: Four
current directions. Artificial Intelligence Magazines,
vol. 18, pp. 97–136.
Doremi Project: http://cordis.europa.eu/project/rcn/110829
_en.html.
Eldergames Project: http://cordis.europa.eu/project/rcn/
80186_en.html.
Fate Project: http://cordis.europa.eu/project/rcn/191694
ICT4AWE 2016 - 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health
94
_en.html.
Foroughi H., Aski B.S., Pourreza H., 2008. Intelligent
video surveillance for monitoring fall detection of
elderly in home environments, 11th International
Conference on Computer and Information
Technology. ICCIT 2008.
Hall, M., Frank, E., Holmes, G., Pfahringer, B.,
Reutemann P., Witten, I. H., 2009. The WEKA Data
Mining Software: An Update. SIGKDD Explorations,
vol. 11.
Home Sweet Home Project: http://cordis.europa.eu/
project/rcn/191712_en.html.
Kastner, M., Strickert, M., Villmann, T., 2013. A sparse
kernelized matrix learning vector quantization model
for human activity recognition. European Symposium
on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN).
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy,
K.R.K., 2001. Improvements to Platt's SMO algorithm
for SVM classifier design. Neural Computation, vol.
13, pp. 637-649.
Kinoptim Project: http://cordis.europa.eu/project/rcn/
106678_en.html.
Kira, K., Rendell, L. A., 1992. A practical approach to
feature selection. Proc. 9th Int. Conf. Mach. Learn.,
pp. 249 – 256.
Kononenko, I., 1994. Estimating attributes: Analysis and
extension of RELIEF. Proc. Euro. Conf. Mach.
Learn., vol. 784, pp. 171– 182.
Kononenko, I., Simec, E., Robnik-Sikonja, M., 1997.
Overcoming the Myopic of Inductive Learning
Algorithms with RELIEF-F. Applied Intelligence.
Mitnitski, A.B., et al., 2002. Frailty, fitness and late-life
mortality in relation to chronological and biological
age. BMC Geriatr, 2: p. 1.
Mobiserv Project: http://cordis.europa.eu/project/rcn/
93537_en.html.
Morley, J.E., et al., 2006. Frailty. Med Clin North Am, .
90(5): p. 837-47.
Morley, J.E., et al., 2013. Frailty consensus: a call to
action. J Am Med Dir Assoc, 14(6): p. 392-7.
Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis,
M., Richardson, M., Megalooikonomou, V., 2015.
Seizure detection using EEG and ECG signals for
computer-based monitoring, analysis and management
of epileptic patients. Expert Systems with Applications,
Volume 42, Issue 6, 15, Pages 3227–3233.
Platt, J., 1998. Fast Training of Support Vector Machines
using Sequential Minimal Optimization. Advances in
Kernel Methods - Support Vector Learning.
Reiss, A., Hendeby, G., Stricker, D., 2013. A competitive
approach for human activity recognition on
smartphones. European Symposium on Artificial
Neural Networks, Computational Intelligence and
Machine Learning (ESANN).
Reyes-Ortiz, J.L., Ghio, A., Parra, X., Anguita, D.,
Cabestany, J., Catala, A., 2013. Human Activity and
Motion Disorder Recognition: Towards Smarter
Interactive Cognitive Environments. ESANN 2013
21th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine
Learning.
Romera-Paredes, B., Aung, H., Bianchi-Berthouze, N.,
2013. A one-vs-one classifier ensemble with majority
voting for activity recognition. European Symposium
on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN).
Seacw Project: http://cordis.europa.eu/project/rcn/191786
_en.html.
Sun, Y., Li, J. 2006. Iterative RELIEF for feature
weighting. Proc. 21st Int. Conf. Mach. Learn., pp.
913–920.
Sun, Y., Wu, D., 2008. A RELIEF based feature extraction
algorithm. In Proceedings of SIAM International
Conference on Data Mining.
Xiang, Y., Tang, Y., Ma, B., Yan, H., Jiang, J., Tian, X.,
2015. Remote Safety Monitoring for Elderly Persons
Based on Omni-Vision Analysis, PLoS One, 10(5).
Feature Selection Evaluation for Light Human Motion Identification in Frailty Monitoring System
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