Content uploaded by Anna Esposito
Author content
All content in this area was uploaded by Anna Esposito on Mar 04, 2017
Content may be subject to copyright.
1 23
Cognitive Computation
ISSN 1866-9956
Cogn Comput
DOI 10.1007/s12559-012-9169-9
Biometric Applications Related to Human
Beings: There Is Life beyond Security
Marcos Faundez-Zanuy, Amir Hussain,
Jiri Mekyska, Enric Sesa-Nogueras,
Enric Monte-Moreno, Anna Esposito,
Mohamed Chetouani, et al.
1 23
Your article is protected by copyright and
all rights are held exclusively by Springer
Science+Business Media, LLC. This e-offprint
is for personal use only and shall not be self-
archived in electronic repositories. If you
wish to self-archive your work, please use the
accepted author’s version for posting to your
own website or your institution’s repository.
You may further deposit the accepted author’s
version on a funder’s repository at a funder’s
request, provided it is not made publicly
available until 12 months after publication.
Biometric Applications Related to Human Beings: There Is Life
beyond Security
Marcos Faundez-Zanuy •Amir Hussain •Jiri Mekyska •Enric Sesa-Nogueras •
Enric Monte-Moreno •Anna Esposito •Mohamed Chetouani •Josep Garre-Olmo •
Andrew Abel •Zdenek Smekal •Karmele Lopez-de-Ipin
˜a
Received: 1 December 2011 / Accepted: 10 July 2012
Springer Science+Business Media, LLC 2012
Abstract The use of biometrics has been successfully
applied to security applications for some time. However,
the extension of other potential applications with the use of
biometric information is a very recent development. This
paper summarizes the field of biometrics and investigates
the potential of utilizing biometrics beyond the presently
limited field of security applications. There are some syn-
ergies that can be established within security-related
applications. These can also be relevant in other fields such
as health and ambient intelligence. This paper describes
these synergies. Overall, this paper highlights some inter-
esting and exciting research areas as well as possible
synergies between different applications using biometric
information.
Keywords Biometrics Security Healthcare Ambient
intelligence
Introduction
The term ‘‘biometrics’’ originates from the Greek words
Bio (life) and metron (measure) and is defined as the sci-
ence and technology of measuring and statistically ana-
lysing biological data. Although many people consider
biometrics only relevant to security applications, in reality,
the relevance of biometrics is very far reaching. This field
has applications relevant to animals, plants and human
beings. Some examples are:
M. Faundez-Zanuy (&)E. Sesa-Nogueras
Escola Universita
`ria Polite
`cnica de Mataro
´, Tecnocampus
Mataro
´Maresme, Avda. Ernest Lluch 32, 08302 Mataro
´, Spain
e-mail: faundez@eupmt.es
E. Sesa-Nogueras
e-mail: sesa@eupmt.es
A. Hussain A. Abel
Department of Computing Science and Mathematics, University
of Stirling, Stirling FK9 4LA, UK
e-mail: ahu@cs.stir.ac.uk
J. Mekyska Z. Smekal
Department of Telecommunications, Brno University of
Technology, Brno, Czech Republic
e-mail: j.mekyska@phd.feec.vutbr.cz
Z. Smekal
e-mail: smekal@feec.vutbr.cz
E. Monte-Moreno
TALP Research Center, UPC, Barcelona, Spain
e-mail: enric.monte@upc.edu
A. Esposito
IIASS (International Institute for Advanced Scientific Studies),
Vietri sul Mare, Italy
e-mail: iiass.annaesp@tin.it
M. Chetouani
Pierre Marie Curie University, Paris, France
e-mail: Mohamed.chetouani@upmc.fr
J. Garre-Olmo
IAS (Sanitary Assistance Institute), Salt, Spain
e-mail: josep.garre@ias.scs.es
K. Lopez-de-Ipin
˜a
EHU (Basque Country University), Donostia, Spain
e-mail: karmele.ipina@ehu.es
123
Cogn Comput
DOI 10.1007/s12559-012-9169-9
Author's personal copy
•Statistical methods for the analysis of data from
agricultural field experiments to compare the yields of
different varieties of wheat.
•Analysis of data from human clinical trials evaluating the
relative effectiveness of competing disease therapies.
•The analysis of biometric characteristics for animal/
human verification or identification.
The main components of a hypothetical biometric
application system are shown in Fig. 1. The first block
deals with the acquisition of input signals. Depending on
the application and the kind of sensors, a variety of dif-
ferent signals may be obtained. Nowadays, most signals are
acquired in a digital format or are converted to digital in
order to make computerized analysis more feasible. While
some signals can be acquired from both human beings and
animals (such as iris and retinal analysis of the eye), others
are specific to humans (such as speech, handwriting, etc.).
This paper is focused exclusively on applications that
are relevant only to human beings. Therefore, we will limit
discussion to only human-specific signals. The set of these
signals can be split into two categories:
1) Behavioural biometrics: this category is based on the
measurements and data derived from an action
performed by a user and thus indirectly measures
some characteristics of human body. Signature, gait,
gesture and key stroking recognition belong to this
category.
2) Morphological biometrics: this category is based on
direct measurements of parts of the human body.
Fingerprint, face, iris and hand-scanning recognition
belong to this category.
However, this classification is quite artificial. For
example, speech signals are dependent on behavioural
traits such as semantics, diction, pronunciation, idiosyn-
crasy, etc. A speech signal might also be related to factors
such as socio-economic status, education, place of birth,
etc. Moreover, it is also dependent on individual speaker
physiology, such as the shape of the vocal tract. On the
other hand, physiological traits are also influenced by
human behaviour, for example, the manner in which a user
presents a finger and looks at a camera, etc.
Figure 2summarizes possible biometric applications as
well as the input signals that can be used for these applica-
tions. While a large set of signals can be utilized for bio-
metric security applications, some offer much more potential
in other fields, especially in the case of behavioural signals.
For the remainder of this paper, we will concentrate exclu-
sively on health and ambient intelligence applications.
Opinion
(score)
MATCHING
decision
features
DECISION-
MAKER
12 3 4
digital input
signal
DATA BASE
(MODELS)
SENSOR FEATURE
EXTRACTION
?
Fig. 1 Main blocks of a
hypothetical biometric
application system
Signals
Biometric applications
write
speech
Gait eye face finger hand …
……
People Plants/ animals
Security
…
…
Health
Amb. Int.
…
Behavioral Morphological
Fig. 2 Summary of main
biometric applications and
possible associated signals.
Each star indicates the
applicability of a given signal
for a specific application
Cogn Comput
123
Author's personal copy
Health Applications
The skill level of humans is strongly related to their health
state. An important example is the way our cognitive
functions are related to the ageing process. Cognitive
decline is a natural part of the ageing process. However, the
extent of decline varies across subjects and across func-
tions. For instance, handwriting and speech production is a
fine motor control performed by our brain. When these
signals are degraded, it is indicatory of health problems.
Figure 3shows the handwriting of one elder person as an
example.
One important unsolved problem is how the dementia
syndrome is associated with diseases such as Parkinson’s
and Alzheimer’s, etc. In the case of Alzheimer’s, it is
estimated that the cost per year for a single patient is
35,000 USD in the USA. One in ten patients is below
60 years old. The incidence of Alzheimer’s is doubled for
every 5 years after 65, and beyond 85 years old, the inci-
dence is between one-third and half of the amount of
population. If a solution is not found, this problem will be
unbearable for society. A relevant issue related to dementia
is its diagnostic procedure. For example, Alzheimer’s dis-
ease (AD) is the most common type of dementia and it has
been pointed out that early detection and diagnosis may
confer several benefits. However, intensive research efforts
to develop a valid and reliable biomarker with enough
accuracy to detect AD in the very mild stages or even in
presymptomatic stages of the disease have not been con-
clusive. Nowadays, the diagnostic procedure includes the
assessment of cognitive functions by using psychometric
instruments such as general or specific tests that assess
several cognitive functions. A typical test for AD is the
clock drawing test (CDT) [84] that consists of drawing a
circle and distributing the 12 h inside. An example of this
is shown in Fig. 4. The top row shows the initial results
produced by a person (baseline) on the left, and on the
right, several samples of the same person after 6, 12 and
18 months of being damaged are also shown. This same
test has also been used for detecting drug abuse, depres-
sion, etc. The bottom row of Fig. 4shows a similar situa-
tion when copying two interlinking pentagons, which is
one of the tasks of the mini-mental state examination
(MMSE) [30]. The MMSE or Folstein test is a brief
30-point questionnaire test that is used to screen for cog-
nitive impairment. It is also used to estimate the severity of
cognitive impairment at a specific time and to follow the
course of cognitive changes in an individual over time, thus
making it an effective way to document an individual’s
response to treatment.
Research by Forbes et al. [31] showed the correlation
between handwriting skill degradation and AD. Initially, it
is possible to detect the disease using handwriting, espe-
cially in the case of cursive letters. Work by Neils-Strunjas
et al. [60] established that some handwriting aspects are
more open to vulnerabilities than others and thus can be
good indicators for AD diagnosis.
Handwriting tests are also very useful for determining
the relevance of medication. For instance, Fig. 5shows on
the left the result of drawing an ellipsoid on a digitizing
tablet. As can be seen, the Yplot, the velocity and accel-
eration of this coordinate are quite periodic for a healthy
person (on the left). In the centre, we can see the results of
a Parkinson disease (PD) patient and on the right a PD
patient taking medication. It is evident that the medication
permits the recovery to a large extent the skill of a healthy
person. Obviously, this kind of analysis can be used for
determining the dosage of drugs for a specific patient. This
example has been extracted from [21]. Similar research
line is exploited here [11].
There are similar experiences using the letter ‘‘ll’’ Tucha
et al. [89,90] and drawing an Archimedes spiral [75].
Werner et al. [94] showed the differences in handwriting
between patients with mild AD and mild cognitive
impairment. Ericsson et al. [23] evaluated the dictated
handwriting and signature and observed that it remained
unaltered longer than spontaneous writing. Heinik et al.
[42] used the drawings for analysing depressive disorders
in older people. Other interesting works using handwriting
include:
a) Changes in handwriting due to Alcohol [27,65]
b) Effects of caffeine on handwriting [90]
c) Effects of marijuana and alcohol [29]
d) Study of kids with perceptive/motor difficulties [48,
72]
Handwriting analysis using a digitizing tablet with an
ink pen has an advantage over the classic method based on
handwriting and posterior scanning, namely that the
machine can acquire the information ‘‘in the air’’. That is,
where there is no contact between pen and paper. Figure 6
shows the acquisition of the ten digits from 1 to 0 using an
Intuos Wacom digitizing tablet (http://www.wacom.eu).
The tablet acquired 100 samples per second including the
spatial coordinates (x,y), the pressure, and a couple of
Fig. 3 Handwriting of an elder person
Cogn Comput
123
Author's personal copy
basal 6 months 12 months 18 months
Fig. 4 Clock drawing test (top),
pentagons of MMSE (bottom)
for a person with AD, showing
initial baseline on the left, and
then from left to right, samples
from the same person after 6,
12, and 18 months
Fig. 5 Signals y(t), v
y
(t), a
y
(t) (position, velocity and acceleration, respectively, of coordinate y) when drawing an ellipsoid by a healthy person
(left), a PD patient (centre) and a PD patient taking apomorphine (APO)
Cogn Comput
123
Author's personal copy
angles (see Figs. 7,8). The pen-up information is repre-
sented in Fig. 6using ‘‘?’’, while the pen-down is marked
with ‘‘*’’. Our experiments on the biometric recognition of
people reveal that these two kinds of information are
complementary and in fact, contain a similar discriminative
capability, even when using a database of 370 users [78,
79].
Speech signals represent another important possibility
for health analysis. Hypokinetic dysarthria is a speech
production alteration based on neurological problems [53].
There are multiple causes for this illness, such as brain
paralysis, thrombosis, embolia, hemorrhagia, tumours and
degenerative diseases (Alzheimer’s, Parkinson’s, Amyo-
trophic lateral sclerosis, etc.). Dysarthria affects speech
quality (articulation, speech, intonation, speed, breath
control, etc.) [57]. One possible analysis based on speech
signals is of emotion analysis, because people affected by
dementia display fewer emotions [80]. Moreau et al. [58]
dealt with oral festination in PD. Festination is the ten-
dency to speed up during repetitive movements. It appears
6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8
x 10 4
4.8
4.81
4.82
4.83
4.84
4.85
4.86
4.87
4.88
4.89
4.9 x 104
Fig. 6 Example of handwritten
numerical digits input onto a
digitizing tablet. Asterisks (*)
represent pen-down information
and crosses (?) the pen-up
90º
90º
180º
270º
0º
Altitude (0º-90º) Azimuth (0º-359º)
Fig. 7 Handwriting angle information acquired by the Intuos Wacom
(X,Y, pressure, Azimuth, Altitude)
0100 200 300 400 500 600
6
6.5
7x 10 4X
0100 200 300 400 500 600
4.8
4.85
4.9 x 10 4Y
0100 200 300 400 500 600
0
2000
4000
Azimuth
0100 200 300 400 500 600
400
600
800 Inclination
0100 200 300 400 500 600
0
500
1000
P
Fig. 8 Temporal evolution of
the acquired parameters when
drawing the numbers shown in
Fig. 6
Cogn Comput
123
Author's personal copy
first with gait in order for sufferers to avoid falling down,
and it subsequently appears in handwriting and speech.
Ozsancak et al. [63] used speech signals to study PD.
Ackermann et al. [5] analysed the trajectory of the lower
lip when articulating speech signals, in order to study
Parkinson’s, Huntington’s, cerebellum atrophia and pseu-
dobulbar paralysis. Goberman and Coelho [36,37],
Nagulic et al. [59], Stewart et al. [83] used speech to
evaluate the improvement of PD after treatment. [93]
analysed the required time taken for sufferers to find the
suitable word as well as time taken to articulate, and they
found that AD specially affected the time taken to find the
correct word, and to a lesser extent the articulation time.
Rapcan et al. [69] used several measures (pitch, energy,
etc.) for schizophrenia detection. They obtained promising
results, which are especially interesting because there are
no biological markers for this kind of disorder. Ferrand
[28] used harmonic-to-noise ratio (HNR), jitter, funda-
mental frequency (F0), etc., and found that the most
relevant of these parameters for studying the ageing pro-
cess is the HNR.
Ringeval et al. [70] developed an automatic intonation
recognition systems exploiting static (e.g. k-NN) and
dynamic classifiers (e.g. HMMs) for the characterization of
verbal productions of language-impaired children. The
main results show that it is possible to characterize the
prosodic abilities of those children and providing results in
agreement with the clinical descriptions of the subjects’
communication impairments.
Figure 9shows a speech sentence pronounced by a
healthy person and the same sentence pronounced by a PD
affected person. It can be seen that the intonation is very
flat for the PD sufferer, matching similar results as those
reported in [41]. AD causes the changes in prosody [71].
The reason is based on the alteration of brain areas devoted
to speech processing [44,62]. In its initial stages, AD can
be confused with multiple sclerosis, and speech analysis
can differentiate between both [9]. Another classical
00.2 0.4 0. 6 0.8 11.2 1.4 1. 6 1.8 2
-1
-0.5
0
0.5
1
t [s]
→
s (t)
→
Healt hy
00.2 0.4 0. 6 0. 8 11.2 1.4 1. 6 1.8 2
-1
-0.5
0
0.5
1
t [s]
→
s (t)
→
Parkinson dis ease
t [s]
→
f [Hz]
→
0.2 0. 4 0.6 0. 8 11.2 1.4 1.6 1.8 2
0
1000
2000
3000
4000
5000
6000
7000
8000
t [s]
→
f [Hz]
→
0.2 0.4 0. 6 0. 8 11.2 1.4 1. 6 1.8
0
1000
2000
3000
4000
5000
6000
7000
8000
00.2 0.4 0. 6 0.8 11.2 1.4 1. 6 1.8 2
80
100
120
140
160
180
200
t [s]
→
F0 [Hz]
→
00.2 0.4 0. 6 0. 8 11.2 1.4 1. 6 1.8 2
80
100
120
140
160
180
200
t [s]
→
F0 [Hz]
→
Fig. 9 Speech sentence uttered by a healthy person (left) and a PD person (right). Waveform (top), spectrogram (middle) and pitch frequency
(bottom)
Cogn Comput
123
Author's personal copy
application of speech processing can be useful for dementia
studies. For instance, AD patients exhibit a reduced
vocabulary [19,47]. Thus, speech recognition applications
can be useful for evaluating the reduction of vocabulary in
spontaneous speech.
Thus, speech signals offer significant potential for
health analysis. Nevertheless, its acquisition can be more
complicated than handwriting due to microphone position,
recording level adjustment, etc. Some studies, such as
[40], studied jointly handwriting and speech, although
they were focused on lexical issues. Obviously, possibil-
ities with other signals exist, such as the pupil reaction to
light [32] and SPECT (single-photon emission computed
tomography) [39], PET (positron emission tomography)
[45], and MRI (magnetic resonance imaging) image
analysis [88].
An interesting application of iris recognition might be to
use this technology for characterizing the relationship of
the change of pupil to the mood state of one person [33]. It
is known that depressed patients manifest a shorter latency
for constriction than control subjects, which is related to
the fact that in depression, the activity of the neurotrans-
mitters’ decreases. Another application of biometric iris
recognition technologies can be to predict the risk of age-
related macular degeneration. Macular degeneration is one
of the main causes of loss of sight in elderly people, and
changes in iris colour are a sign of the risk of this illness
[43]. Another extremely promising use of biometric tech-
nologies can be for a noninvasive estimate of cholesterol,
through the changes in the iris of a patient [67].
Another potential use for biometric information is to
develop the next generation of hearing aids. The previous
audio-only developments in the field of speech enhance-
ment (such as multi-microphone arrays and speech
enhancement algorithms) have been developed academi-
cally and then been implemented into commercial hearing
aids for the benefit of the hearing impaired community. In
recent years, hardware has developed to an extent that very
sophisticated multiple microphone hearing aids have been
developed that exclusively exploit the audio modality. It is
expected that in the future, conventional hearing aids will
be transformed to make use of visual information with the
aid of cameras for input in addition to conventional audio
input, demonstrating that it is possible to combine audio
and visual information to further improve the quality and
intelligibility of speech in real-world noisy environments.
Speech is produced by vibration of the vocal cord and
the configuration of the vocal tract that is composed of
articulatory organs, and due to the visibility of some of
these articulators such as tongue, teeth, and lips, there is an
inherent relationship between the acoustic and visible
properties of speech production. The speech percep-
tion connection between audio and visual aspects of
communication has been established since pioneering
works in 1954 [87] and subsequent developments such as
the McGurk effect [56]. In addition, audiovisual speech
correlation has been deeply investigated in the literature [7,
8,74], including in the work by [4,18], showing the
connection between lip movement and acoustic speech and
that this connection could be used for enhancing noisy
speech.
Multimodal correlation is of interest because of the
application of visual information to the speech enhance-
ment domain. To the best knowledge of the authors, the
first example of a functioning audiovisual speech filtering
system was proposed in 2001 [35], and this was then fol-
lowed by other related work [38,81,82]. The increased
processing power of computers and the miniaturized and
improved capability of relevant technical components such
as video cameras and processors have made the concept of
utilizing cameras for speech processing, possibly even as
part of a hearing aid system, much more feasible. There are
both strengths and weaknesses with the use of visual
information for speech enhancement, but it has proved
practical for further development. Following the pioneering
work by Girin et al. [35], more recent work has focused on
the use of visual information as part of a source separation-
based system [81,82]. In addition, [6] has made use of
visual information as part of a Wiener filtering speech
processing system. The use of visual biometric informa-
tion, applied intelligently, has the potential to improve the
quality of future hearing aid devices and aid the lives of
those who suffer from hearing impairment.
Multimodal signal processing plays an important role in
human communication analysis due to its integrative pro-
cess. Indeed, correlations between speech and visual
information (e.g. gestures, movements) make it possible to
extract intra- and inter-coordination. In Delaherche and
Chetouani [20], a general framework is proposed for the
characterization of dyadic interactions for the automatic
assessment of the interactional synchrony, which is con-
sidered as a measure of the quality of interaction.
Ambient Intelligence
In computing, ambient intelligence refers to electronic
environments that are sensitive and responsive to the
presence of people. According to [1,96], it is characterized
by systems and technologies that are:
•Embedded: many networked devices are integrated into
the environment;
•Context aware: these devices can recognize you and
your situational context;
•Personalized: they can be tailored to your needs;
•Adaptive: they can change in response to you;
Cogn Comput
123
Author's personal copy
•Anticipatory: they can anticipate your desires without
conscious mediation.
While probably the highest level of intelligence that a
machine can posses is the knowledge about the health
condition of the human beings in front of machine, there is
much other possible information that the machine can
infer, such as
•Who is in front of the machine? (man/woman)
•How old are they? (child, elder, etc.)
•What is their emotional state? (angry/sad/happy, etc.)
•Who is speaking in a given room?
In a daily body-to-body interaction, emotional expres-
sions play a vital role in creating social linkages, producing
cultural exchanges, influencing relationships and communi-
cating experiences. Emotional information is transmitted and
perceived simultaneously through verbal (the semantic
content of a message) and nonverbal (facial expressions,
vocal expressions, gestures, paralinguistic information)
communicative tools, and contacts and interactions are
highly affected by the way this information is communicated/
perceived by/from the addresser/addressee. Therefore,
research devoted to the understanding of the relationship
between verbal and nonverbal communication modes, and to
investigate the perceptual and cognitive processes involved
in the perception of emotional states, as well as the role
played by communication impairments in their recognition,
is particularly relevant in the field of human–human and
human–computer Interaction both for building up and
hardening human relationships and for developing friendly
and emotionally coloured assistive technologies.
Emotions are considered as adaptive reactions to rele-
vant changes in the environment, which are communicated
through a nonverbal code from one organism to another
[66]. This perspective is based on several assumptions,
among which, the most important is that there exists a
small set of universally shared discrete emotional catego-
ries from which other emotions can be derived [22,46].
This small set of emotional categories includes happiness,
anger, sadness and fear, which can be reliably associated
with basic survival problems such as nurturing offspring,
earning food, competing for resources, avoiding and/or
facing dangers. In this context, basic emotions are brief,
intense and adapted reactions to urgent and demanding
survival issues. These reactions to goal-relevant changes in
the environment require ‘‘readiness to act’’ and ‘‘prompting
of plans’’ in order to appropriately handle (under condi-
tions of limited time) the incoming event producing suit-
able mental states, physiological changes, feelings and
expressions [34].
The categorization of emotions is, however, debated
among researchers and different theories have been
proposed for its conceptualization, among these dimen-
sional models [73,77]. Such models envisage a finite set of
primary features (dimensions) in which emotions can be
decomposed and suggest that different combinations of
such features can arouse different affective states. Bringing
the dimensional concept to an extreme, such theories
suggest that, if the number of primary features extends
along a continuum, it would be possible to generate an
infinite number of affective states. This idea, even though
intriguing, clashes with the principle of economy that
seems to rule the dynamic of natural systems, since in this
case, the evaluation of affective states may require an
infinite computational time. Moreover, humans tend to
categorize, since it allows for them to make associations,
rapid recovery of information, and facilitates handling of
unexpected events, and therefore, categories may be
favoured in order to avoid excessive processing time.
Furthermore, this discrete evolutionary perspective of basic
emotions has been supported through several sources of
evidence, such as the findings of (1) an emotion-specific
autonomic nervous system’s (ANS) activity
1
[50]; (2)
distinct regions of the brain tuned to handle basic emotions
[64]; (3) presence of basic emotional expressions in other
mammalian species (as the attachment of infant mammals
to their mothers) [61]; (4) universal exhibition of emotional
expressions (such as smiling, amusement and irritability)
by infants, adults, blind and sighted [61]; (5) universal
accuracy in recognizing facial and vocal expressions of
basic emotions by all human beings independently of race
and culture [22,46,76].
Most of the relevant applications in information com-
munication technologies exploit what are called the
‘‘expressions of emotions’’, that is, changes in expressions
that allow interactants to perceive an emotional state during
face-to-face interaction. In this sense, the perceptual
appearance of emotional states is attributed to perceptual
changes in the facial, vocal and gestural expressions [24–
26].
In the field of human computer interface (HCI), the
research objectives are to identify methods and procedures
capable of automatically identifying human emotional
states exploiting the multimodal nature of emotions. This
requires the consideration of several key aspects, such as
the development and the integration of algorithms and
procedures for applications in communication, and for the
recognition of emotional states, from gestures, speech, gaze
and facial expressions, in anticipation of the implementa-
tion of intelligent avatars and interactive dialog systems
1
It should be noticed that not all these findings proved to be strong
enough, as, for example, [10,13] disconfirmed the existence of an
autonomic specificity and distinctive ANS’s activity patterns for each
basic emotion.
Cogn Comput
123
Author's personal copy
that could be exploited to improve the learning and
understanding of emotional behaviour and facilitate the
user’s access to future communication services.
Emotional processes in disabilities and health disorders
follow in some aspects the same paths exploited in typical
normal conditions and are different in other aspects.
Impairments and developmental disorders may change
emotional expressions and needs with respect to normal
emotional processes.
Emotional reactions may be different. Questions on how
these differences are expressed, felt and relevant to social
interaction are still open and can be considered to still be at
a theoretical level. Comparing normative and disordered
expressions of emotional states can be useful not only for
implementing effective intelligent systems able to interact
with disabled people, but also to improve the performance
of these systems. To the best of our knowledge, very little
research has been done up to now in this direction.
Given the complexity of the problem, there has been a
branching of the engineering approach toward the
improvement and the development of video–audio tech-
niques, such as video and image processing, video and
image recognition, synthesis and speech recognition, object
and features extraction from audio and video, with the goal
of developing new cutting edge methodologies for syn-
thesizing, analysing and recognizing emotional states from
faces, speech and/or body movements.
One example of an emerging ambient intelligence
technology is the novel emotion and sentiment mining
approach, termed sentic computing, that has been devel-
oped by Cambria and Hussain et al. [14], which aims to
extract cognitive and affective information associated
with natural language and, hence, better understand the
current state of the user, including factors such as his/her
emotional state, current needs and intent. Cambria et al.
[14] also employed affective ontologies and common
sense reasoning tools to analyse text not only at docu-
ment, page or paragraph level, but also at sentence and
clause level.
Sentic computing involves the use of AI and Semantic
Web techniques, for knowledge representation and infer-
ence; mathematics, for carrying out tasks such as graph
mining and multi-dimensionality reduction; linguistics, for
discourse analysis and pragmatics; psychology, for cogni-
tive and affective modelling; sociology, for understanding
social network dynamics and social influence; and finally
ethics, for understanding related issues about the nature of
the mind and the creation of emotional machines.
In the field of health, in particular, sentic computing has
been used for the development of patient-centred applica-
tions [15], which empower the real end-users of the health
system by bridging the gap between unstructured and
structured health-care data [16]. Sentic computing is also
employed for the development of intelligent multimodal
affective interfaces, in which many different technologies
are concurrently applied and integrated, for example, a
facial emotional classifier and a multimodal animation
engine for managing virtual agents and 3D scenarios [17].
Different sensors usable in a home environment are
nowadays available at reasonable prices. In the last few
years, many efforts have been made to build different
frameworks capable of integrating unstructured signals
received from different sources. The main aim of such
systems is to enhance everyday living (e.g. for home
automation system) but also to allow people who require
care to safely live in their home environment [55].
An interesting example of ambient intelligence has been
given in Rantz et al. [68] where a number of sensors have
been installed in an apartment within a retirement com-
munity. The sensor network (including bed, chair, stove
temperature and motion sensors) passively collected data to
detect the presence of the person in different rooms and to
infer when the person is carrying out specific activities.
Data from sensors are then aggregated for each patient and
made available to clinicians and researchers; graphical
representations of the activity level could help healthcare
providers to detect any changes in activity patterns, after
receiving automated alert from the system. It has been
shown the potential of this kind of system for early
detection of specific pathologies (e.g. for urinary treat
infections).
There are a number of diverse applications for ambient
intelligence-based technologies and systems that are
expected to impact our daily life in the future. For example,
consider the case of future intelligent transportation sys-
tems for tackling drink driving. One hypothetical example
could be a scenario where a driver intends to drive his/her
car after a night out drinking with friends, an embedded
ambient intelligent system within his car will be able to
automatically detect this situation and judge whether the
level of alcohol is below the legal limit or not. If the system
finds the driver might be illegally driving, it may send a
signal to alert the driver and in an extra case it can stop the
driver from starting his/her car. Another potential appli-
cation of intelligent transport is where ambient intelligence
can also help to alert the driver to be aware of speeding if
the intelligent system can recognise the driving situation
and detect the speed limit.
In adaptive cruise and steering control (ACC) of future
cognitive or ‘‘smart’’ vehicles, ambient intelligence may
also play a key role. Figure 10 illustrates such a typical
system where an intelligent multivariable multi-controller
approach is employed to realize speed tracking by using a
longitudinal and lateral vehicle model and a switching
strategy from one mode to another [2,3]. One example is
given in Fig. 11 showing how the intelligent system is able
Cogn Comput
123
Author's personal copy
to track the target speed of vehicle by switching the con-
trollers between two modes [2,3].
Synergies and Interactions between Health and Security
Applications
Ideally, a system with ambient intelligence should be able
to detect the age of the persons in front of it, and their
gender, health condition, emotional state, etc. This infor-
mation should be inferred from the signals described in
Section 2. Some security systems are also able to detect
heart rates. If the heart rate is higher than a predetermined
threshold, a silent alarm is activated because the system
considers that the person is providing their biometric trait
in a situation where they may be under duress.
One of the main concerns of biometrics applied to
security is about privacy issues. Technological advances let
to store, gather and compare a wide range of information
on people. Using identifiers such as name, address, pass-
port or social security number, institutions can search
databases for individuals’ information. This information
can be related to salary, employment, sexual preferences,
religion, consumption habits, medical history, etc. Though
in most of the scenarios there should be no problem, there
is a potential risk. Let us think, for instance, in sharing
medical information. Obviously, in case of emergency, this
sharing between hospitals would be beneficial. On the
contrary, if this information is transferred to a personal
insurance company or a prospective employer, the insur-
ance or the job application can be denied. The situation is
especially dramatic when biometric data collection is
intended for security purposes but a third party tries to infer
the health condition of the subject. For instance, in the case
of retina and iris recognition, an expert can determine that a
patient suffers from diabetes, arteriosclerosis, hyperten-
sion, etc.
For any biometric identifier, there is a portion of pop-
ulation for which it is possible to extract relevant infor-
mation about their health, with similar implications to the
ones described in previous paragraph, for example, speech
disorders, hair or skin colour problems, etc. An important
question is what exactly is disclosed when biometric
scanning is used. In some cases, additional information not
related to identification might be obtained. For instance,
[95] presents a list of these cases that includes
•Some studies suggesting that fingerprints and finger
images may disclose medical information about a
person (chromosomal disorders such as Down syn-
drome, Turner syndrome and Klinefelter syndrome, and
nonchromosomal disorders, such as chronic, intestinal
pseudo-obstruction, leukaemia, breast cancer and
Rubella syndrome).
•Several researchers reporting a link between finger-
prints and homosexuality.
Fig. 10 Intelligent
multivariable multi controller
approach to adaptive cruise
control
Cogn Comput
123
Author's personal copy
In (Maltoni et al. [54], p. 46), there is a set of references
about statistical correlation between malformed fingers and
certain genetic disorders.
While the relationship between genetic disorders and
fingerprints may be possible, it is hard to believe that a
fingerprint, which is fully formed at about 7 months of
foetus development and does not change throughout the
life of an individual (Maltoni et al. [54], p. 24), could be
correlated with sexual preferences that can vary, or dis-
eases that can appear and disappear during a lifetime.
Most biometric traits evolve through time. Feature
extraction is a key point of classification, but nowadays
there are no powerful studies about the evolution of dif-
ferent parameters: Which are more long lasting? Which
phenomena affect these parameters? How can we use this
information for a robust biometric security/health appli-
cation? Is the person in front of the machine in good health
condition and he/she can be responsible of his/her own
acts? Fig. 12 shows a real case extracted from [92]. In this
case, several women made an elder woman sign her name
on blank sheets of paper (Fig. 13). Theoretically, it was to
solve some issues related to medicines. When the elder
person died, the other women took advantage of the signed
sheets in order to write a rental agreement. The theoretical
date of this agreement was 1985 (Fig. 12 on the bottom),
but several documents signed in 1986 (Fig. 12 on the top)
showed better control of calligraphic movements. In fact,
the hesitantly written signature document signed in 1985
was closer in appearance to the blank sheets signed when
the elder woman had dementia than to the 1986 document.
Thus, it was demonstrated that in fact the rental document
was not signed in 1985. It was signed later.
An interesting application of biometric system com-
bined with ambient intelligence to health is the use of gait
recognition for predicting falls of elderly people. From a
healthcare perspective, different applications for exploiting
ambient intelligence have been recently proposed. Among
them, we can recall here an automated fall detection system
[52] whose main aim is to promptly detect falls especially
in older people to ensure a rapid medical intervention. In
that study, falls are reported as the leading causes of
accidental death in the US population over 65, with a large
percentage of all people who died as a result of a fall being
over 65. An inexpensive system based on Doppler radar
sensors has been set up and a k-NN (nearest neighbour)-
based classification system has been developed showing
Fig. 11 Intelligent multiple
controller in tracking target
vehicle speed: (a) output speed
trajectory, (b) multiple
controller switching scheme
among throttle and wheel brake
subsystems
Cogn Comput
123
Author's personal copy
excellent performance (with an AUC equal to 0.96) in
detecting falls at home.
On a similar topic, [86] proposed a preliminary study on
a depth camera device in home environments with a view
of building a fall risk model. That study has shown how
measurements of temporal and spatial gait parameters
could be inexpensively and passively (i.e. without the
active involvement of the person being observed) obtained
by a depth camera (such the popular Microsoft Kinect)
combined with a motion capture system for ground truth.
In biometric systems, the use of gait information has
been used not only for recognizing the identity of people
but also for indentifying gender [49]. The technology for
gait recognition is based on deriving parameters from sil-
houettes, such as approximating ellipses, from which time-
dependent features are extracted, which are fed into a
classifier that gives as output the identity and/or the gender
of the person in the image.
A straight forward extension of this idea might be to
determine a specific gait sequence of a given person, and
detect whether the gait process suffers changes. In the case
of elderly people, it might be a good predictor of the
probability of falling [91]. In this paper, the authors ana-
lysed the set of features that gave the best prediction of the
risk of falling, which from a set of 7 gait markers, the
feature that explained most of the variance was a slower
gait speed. The technology for detecting gait anomalies
does not need to be based on expensive video signal pro-
cessing, but can be based on simple accelerometers [51],
which can be implemented in a wrist wearable device or
even on a mobile telephone. The same technology can be
used for training and correcting the gait of elderly people
and, therefore, diminishing the fall risk [85].
The use of the current technology on gait recognition
not only can identify the danger of fall, but also can be used
to train elderly people to reduce the risk of falling. As a
matter of fact, the combination of biometric and ambient
intelligence technologies may allow to improve the quality
of living and autonomy of elderly or handicapped people,
which improves the independence and auto-sufficiency and
at the same time might lower the cost of attending a
growing fraction of the population that needs specific care,
but not on a 24 h basis.
A last straightforward question is about the physical
‘‘apparent’’ age and the real age. For instance, [12] reveal a
loss of writing speed in later life, particularly in individuals
suffering from senile psychoses. The differences in writing
speed between senile subjects and ‘‘normal elderly’’ ones
were less than the differences between normal elderly and
young subjects. They also provide a plot that relates age
with writing speed. Thus, theoretically, an apparent age
estimation is possible looking at the writing speed, and
some categorization of people could be possible: those with
health condition below the average of those born the same
year and those in better condition than the average. This
classification could probably be considered very sensible
and private data.
Conclusions
In this paper, we have discussed several applications of
biometrics related to human beings beyond security
applications. Mainly, we have investigated the possibilities
in health and ambient intelligence, as well as the rela-
tionship between these applications. The most important
issue is that the same signals used for security applications
can be used for detecting diseases such as dementia, drug
abuse, diabetes, arteriosclerosis, hypertension, genetic
disorders, etc. This is a double-edged sword because bio-
metric data can be used to assist in obtaining accurate and
fast health diagnoses, but this information can also be
illegally inferred without the consent of the user. Another
synergy worth considering is when health issues are
important for identity verification, that is, when the health
Fig. 12 Documents signed in 1985 (hesitated) and 1986
Cogn Comput
123
Author's personal copy
state can change the validity of the authentication. These
are the cases where the user has provided his biometric
data, such as the signature, under pressure or affected by
dementia.
Thus, this is a hot research topic that must be addressed,
probably by signal processing teams cooperating with
medical doctors and working on both biometric research
fields: health and security.
Acknowledgments This work was supported by FEDER and MEC,
TEC2009-14123-C04-04. SIX (CZ.1.05/2.1.00/03.0072), CZ.1.07/2.3.00/
20.0094, VG20102014033, GACR 102/12/1104 and KONTAKT
ME10123. We also thank Francesc Vin
˜als and Mari Luz Puente for
providing the examples in Figs. 12 and 13.
References
1. Aarts E, Harwig R, Schuurmans M. Ambient intelligence. In:
Denning PJ, editor. The Invisible future: the seamless integration
of technology into everyday life. New York: McGraw-Hill; 2001.
p. 235–50.
2. Abdullah R. Intelligent methods for complex systems control
engineering. PhD thesis with Dr Amir Hussain. UK: The
University of Stirling; 2007.
3. Abdullah R, Hussain A, Warwick K, Zayed A. Autonomous
intelligent cruise control using a novel multiple-controller
framework incorporating fuzzy-logic-based switching and tuning.
Neurocomputing. 2008;71:2727–41.
4. Abel A, Hussain A, Nguyen QD, Ringeval F, Chetouani M,
Milgram M. Maximising audiovisual correlation with automatic
lip tracking and vowel based segmentation. In: Biometric ID,
editor. Management and multimodal communication. Berlin:
Springer; 2009. p. 65–72.
5. Ackermann H, Hertich I, Daum I, Scharf G, Spieker S. Kinematic
analysis of articularoty movements in central motor disorders.
Mov Disord. 1997;12(6):1019–27.
6. Almajai I, Milner B. Effective visually-derived Wiener filtering
for audio-visual speech processing. In: Proceedings of the inter-
speech. Brighton, UK; 2009.
7. Almajai I, Milner B. Maximising audio-visual speech correlation.
In: Proceedings of the AVSP. 2007.
8. Barker JP, Berthommier F. Evidence of correlation between
acoustic and visual features of speech. In: Proceedings of the
ICPhS ‘99. 1999; p. 199–202.
9. Beatty WW, Orbelo DM, Sorocco KH, Ross ED. Comprehension
of affective prosody in multiple sclerosis. Multiple Scler J.
2003;9(2):148–53.
10. Bermond B, Nieuwenhuyse B, Fasotti L, Schuerman J. Spinal
cord lesions, peripheral feedback, and intensities of emotional
feelings. Cognit Emot. 1991;5:201–20.
11. Bidet-Ildei C, Pollak P, Kandel S, Fraix V, Orliaguet J-P.
Handwriting in patients with parkinson disease: effect of l-dopa
and stimulation of the sub-thalamic nucleus on motor anticipa-
tion. Hum Mov Sci. 2011;30(4):783–91.
12. Birren JE, Botwinick J. The relation of writing speed to age and
to the senile psychoses. J Consult Psychol. 1951;15(3):243–9.
Fig. 13 Some signatures on
blank sheets, when the elder
woman was suffering dementia
Cogn Comput
123
Author's personal copy
13. Caccioppo JT, Klein DJ, Bernston GC, Hatfield E. The psycho-
physiology of emotion. In: Lewis JM, Haviland-Jones M, editors.
Handbook of emotion. New York: Guilford Press; 1993.
p. 119–42.
14. Cambria E, Hussain A, Havasi C, Eckl C. Sentic computing:
exploitation of common sense for the development of emotion-
sensitive systems. Lect Notes Comput Sci. 2010;5967:148–56.
15. Cambria E, Hussain A, Durrani T, Havasi C, Eckl C, Munro J.
Sentic computing for patient centered applications. In: 10th IEEE
international conference on signal processing (ICSP), 2010.
p. 1279–82.
16. Cambria E, Hussain A, Eckl C. Bridging the gap between
structured and unstructured health-care data through semantics
and sentics. In: Proceedings of the ACM WebSci’11; 2011.
p. 1–4.
17. Cambria E, Hupont I, Hussain A, Cerezo E, Baldassarri S. Sentic
avatar: multimodal affective conversational agent with common
sense. LNCS, vol. 6456. Berlin, Heidelberg: Springer; 2011.
p. 81–95.
18. Cifani S, Abel A, Hussain A, Squartini S, Piazza F. An investi-
gation into audiovisual speech correlation in reverberant noisy
environments. Lect Notes Comput Sci. 2009;5641:331–43.
19. Chan D, Fox NC, Scahill RI, Crum WR, Whitwell JL, Leschziner
G, Rossor AM, Stevens JM, Ciplolotti L, Rossor MN. Patterns on
temporal lobe atrophy in semantic dementia and Alzheimer’s
disease. Ann Neurol. 2001;49:433–42.
20. Delaherche E, Chetouani M. Multimodal coordination: exploring
relevant features and measures. In: SSPW ’10 Proceedings of the
2nd international workshop on Social signal processing; 2010.
p. 47–52.
21. Eichhorn TE, Gasser T, Mai N, Marquardt C, Arnold G,
Schwarzy J, Oertel WH. Computational analysis of open loop
handwriting movements in Parkinson’s disease: a rapid method to
detect dopamimetic effects. Mov Disord. 1996;11(3):289–97.
22. Ekman P. An argument for basic emotions. Cognit Emot.
1992;6:169–200.
23. Ericsson K, Forssell LG, Holme
´n K, Viitanen M, Winblad B.
Copying and handwriting ability in the screening of cognitive
dysfunction in old age. Arch Gerontol Geriatr. 1996;22:103–21.
24. Esposito A. The amount of information on emotional states
conveyed by the verbal and nonverbal channels: some perceptual
data. In: Stilianou Y, et al., editors. Progress in nonlinear speech
processing. Lecture notes in computer science, vol. 4392. Berlin:
Springer; 2007. p. 45–264.
25. Esposito A. Affect in multimodal information. In: Tao J, Tan T,
editors. Affective information processing. Heidelberg: Springer;
2008. p. 211–34.
26. Esposito A. The perceptual and cognitive role of visual and
auditory channels in conveying emotional information. Cogn
Comput J. 2009;1(2):268–78.
27. Faruk A, Turan N. Handwritten changes under the effect of
alcohol. Forensic Sci Int. 2003;132(3):201–10.
28. Ferrand C. Harmonics-to-noise ratio: an indexing of vocal aging.
J Voice. 2002;16(4):480–7.
29. Foley RG, Miller L. The effects of marijuana and alcohol usage
on handwriting. Forensic Sci Int. 1979;14(3):159–64.
30. Folstein MF, Folstein SE, McHugh PR. Mini mental state: a
practical method for grading the cognitive state of patients for the
clinician. J Psychiatr Res. 1975;12:189–98.
31. Forbes KE, Shanks MF, Venneri A. The evolution of dysgraphia
in Alzheimer’s disease. Brain Res Bull. 2004;63:19–24.
32. Fotiou DF, Stergiou V, Tsiptsios D, Lithari C, Nakou M,
Karlovasitou A. Cholinergic deficiency in Alzheimer’s and Par-
kinson’s disease: evaluation with pupillometry. Int J Psycho-
physiol. 2009;73(2):143–9.
33. Fountoulakis K, Fotiou F, Iacovides A, Tsiptsios J, Goulas A,
Tsolaki M, Ierodiakonou C. Changes in pupil reaction to light in
melancholic patients. Int J Psychophysiol. 1999;31(2):121–8.
ISSN 0167-8760.
34. Frijda NH. Moods, emotion episodes, and emotions. In: Lewis
JM, Haviland-Jones M, editors. Handbook of emotion. New
York: Guilford Press; 1993. p. 381–402.
35. Girin L, Schwartz JL, Feng G. Audio-visual enhancement of
speech in noise. J Acoust Soc Am. 2001;109(6):3007–20.
36. Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian
speech I: speech characteristics and L-Dopa therapy. NeuroRe-
habilitation. 2002;17:237–46.
37. Gobermana AM, Coelho C. Acoustic analysis of Parkinsonian
speech II: L-Dopa related fluctuations and methodological issues.
NeuroRehabilitation. 2002;17:247–54.
38. Goecke R, Potamianos G, Neti C. Noisy audio feature enhance-
ment using audio-visual speech data. In: Acoustics, speech, and
signal processing, Proceedings (ICASSP’02), vol. 2. IEEE
International Conference; 2002. p. 2025–2028.
39. Gorriz JM, Segovia F, Ramirez J, Lassl A, Salas-Gonzalez D.
GMM based SPECT image classification for the diagnosis of
Alzheimer’s disease. Appl Soft Comput. 2011;11:2313–25.
40. Groves-Wright K, Neils-Strunjas J, Burnett R, O’Neill MJ. A
comparison of verbal and written language in Alzheimer’s dis-
ease. J Commun Disord. 2004;37(2):109–30.
41. Gustaw K, Gonet W. Speech disorders in multiple system atrophy
of parkinson type. Clin Res. 2008;1(2):185–8.
42. Heinik J, Werner P, Dekel T, Gurevitz I, Rosenblum S. Com-
puterized kinematic analysis of the clock drawing task in elderly
people with mild major depressive disorder: an exploratory study.
Int Psychogeriatr. 2010;22(3):479–88.
43. Holz FG, Piguet B, Minassian DC, Bird AC, Weale RA.
Decreasing stromal iris pigmentation as a risk factor for age-
related macular degeneration. Am J Ophthalmol. 1994;117(1):
19–23.
44. Iliadou V, Kaprinis S. Clinical psychoacoustics in Alzheimer’s
disease central auditory processing disorders and speech deteri-
oration. Ann Gen Hospital Psychiatr. 2003;2:12.
45. Illa
´n IA, Go
´rriz JM, Ramı
´rez J, Salas-Gonzalez D, Lo
´pez MM,
Segovia F, Chaves R, Go
´mez-Rio M, Puntonet CG, the Alzhei-
mer’s Disease Neuroimaging Initiative. 18F-FDG PET imaging
analysis for computer aided Alzheimer’s diagnosis. Inf Sci.
2011;181(4):903–16.
46. Izard CE. Innate and universal facial expressions: evidence from
developmental and cross-cultural research. Psycholog Bull.
1994;115:288–99.
47. Kempler D, Curtiss S. Catherine jackson ‘‘synthactic preservation
in Alzheimer’s disease’’. J speech Hearing Res. 1987;30:343–50.
48. Kushki A, Chau T, Anagnostou E. Handwriting difficulties in
children with autism spectrum disorders: a scoping review.
J Autism Dev Disord. 2011;41(12):1706–16.
49. Lee L, Grimson WEL. Gait analysis for recognition and classi-
fication. Automatic face and gesture recognition, 2002. In: Pro-
ceedings of the fifth IEEE international conference; 2002.
p. 148–55.
50. Levenson RW. Human emotion: a functional view. In: Ekman
PP, Davidson RJ, editors. The nature of emotion: fundamental
questions. New York: Oxford University Press; 1994. p. 123–6.
51. Liu R, Zhou J, Liu M, Hou X. A wearable acceleration sensor
system for gait recognition. In: 2nd IEEE Conference on indus-
trial electronics and applications, ICIEA 2007; 2007. p. 2654–59.
52. Liu L, Popescu M, Rantz M, Skubic M, Cuddihy P, Yardibi T.
Automatic fall detection based on Doppler radar motion signa-
ture. In: 5th International conference on pervasive computing
technologies for healthcare; 2011. p. 222–5.
Cogn Comput
123
Author's personal copy
53. Llau Arcusa MJ, Gonzalez Alvarez J. Medida de la inteligibilidad
en el habla disaa
´rtrica. Rev Logop Foniatr Audiol. 2004;24:
33–43.
54. Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fin-
gerprint recognition. 1st ed. New York: Springer; 2003.
55. Maternaghan C, Turner KJ. A component framework for telecare
and home automation. In: CCNC’10 Proceedings of the 7th IEEE
conference on consumer communications and networking con-
ference; 2009. p. 886–870.
56. McGurk H, MacDonald J. Hearing lips and seeing voices. Nature.
1976;264:746–8.
57. Mekyska J, Smekal Z, Kostalova M, Mrackova M, Skutilova S,
Rektorova I. Motor aspects of speech imparment in Parkinson’s
disease and their assessment. Cesk Slov Neurol N. 2011;74:
662–8.
58. Moreau C, Ozsancak C, Blatt J-L, Derambure P, Destee A,
Defebvre L. Oral festination in parkinson’s disease: biomechan-
ical analysis and correlation with festination and freezing of gait.
Mov Disord. 2007;22(10):1503–6.
59. Nagulic M, Davidovic J, Nagulic I. Parkinsonian voice acoustic
analysis in real-time after stereotactic thalamotomy. Stereotact
Funct Neurosurg. 2005;83(2–3):115–21.
60. Neils-Strunjas J, Groves-Wright K, Mashima P, Harnish S.
Dyspgraphia in Alzheimer’s disease: a review for clinical and
research purposes. J speech Lang Hearing Res. 2006;49(6):
1313–30.
61. Oatley K, Jenkins JM. Understanding emotions. 2nd ed. Oxford:
Blackwell; 2006.
62. Ohn TG, Braak H. Auditory brainstem nuclei in Alzheimer’s
disease. Neurosci Lett. 1989;2:60–3.
63. Ozsancak C, Auzou P, Jan M, Defebvre L, Derambure P, Destee
A. The place of perceptual analysis of dysarthria in the differ-
ential diagnosis of corticobasal degeneration and Parkinson’s
disease. J Neurol. 2006;253(1):92–7.
64. Panksepp J. Emotions as natural kinds within the mammalian
brain. In: Lewis JM, Haviland-Jones M, editors. Handbook of
emotions. 2nd ed. New York: Guilford Press; 2000. p. 137–56.
65. Phillips JG, Ogeil RP, Muller F. Alcohol consumption and
handwriting: a kinematic analysis. Hum Mov Sci. 2009;28:
619–32.
66. Plutchik R. Emotions as adaptive reactions: implications for
therapy. Psychoanal Rev LIII. 1966;2:105–10.
67. Ramlee RA, Ranjit S. Using iris recognition algorithm, detecting
cholesterol presence. In: International conference on information
management and engineering; 2009. p. 714–7.
68. Rantz MJ, Skubic M, Koopman RJ, Phillips L, Alexander GL,
Miller SJ, Guevara RD. Using sensor networks to detect urinary
tract infections in older adults. In: Proceedings of the IEEE 13th
international conference on e-health networking, applications and
services. 2011.
69. Rapcan V, D’Arcy S, Yeap S, Afzal N, Thakore J, Reilly RB.
Acoustic and temporal analysis of speech: a potential biomarker
for schizophrenia. Med Eng Phys. 2010;32(9):1074–9.
70. Ringeval F, Demouy J, Szaszak G, Chetouan M, Robel L, Xavier
J, Cohen D, Plaza M. Automatic intonation recognition for the
prosodic assessment of language impaired children. IEEE Trans
Audio Speech Lang Process. 2011;19(5):1328–42.
71. Roberts VJ, Ingram SM, Lamar M. Prosody impairment and
associated affective and behavioral disturbances in Alzheimer’s
disease. Neurology. 1996;47:1482–8.
72. Rosenblum S, Parush S, Weiss PL. The in air phenomenon:
temporal and spatial correlates of the handwriting process. Per-
cept Mot Skills. 2003;96(3):933–54.
73. Russell JA. A circumplex model of affect. J Pers Soc Psychol.
1980;39:1161–78.
74. Sargin ME, Yemez Y, Erzin E, Tekalp AM. Audiovisual syn-
chronization and fusion using canonical correlation analysis’’.
IEEE Trans Multimed. 2007;9(7):1396–403.
75. Saunder-Pullman R, Derbym C, Stanley K, Floyd A, Bressman S,
Lipton RB, Deligtisch A, Severt L, Qiping Yu, Kurtis M, Pullman
SL. Validity of spiral analysis in early Parkinson’s disease. Mov
Disord. 2008;23(4):531–7.
76. Scherer KR, Banse R, Wallbott HG. Emotion inferences from
vocal expression correlate across languages and cultures. J Cross
Cult Psychol. 2001;32:76–92.
77. Schlosberg H. Three dimensions of emotion. Psychol Rev.
1953;61(2):81–8.
78. Sesa E, Faundez-Zanuy M. Biometric recognition using online
uppercase handwritten text. Pattern Recognit. 2012;45(1):
128–44.
79. Sesa E, Faundez-Zanuy M, Mekyska J. An information analysis
of in-air and on-surface trajectories in online handwriting. Cognit
Comput. 2012;4:195–205.
80. Skodda S, Schlegel U. Speech rate and rhythm in parkinson’s
disease. Mov Disord. 2008;23(7):985–92.
81. Sodoyer D, Schwartz JL, Girin L, Klinkisch J, Jutten C. Sepa-
ration of audio-visual speech sources: a new approach exploiting
the audio-visual coherence of speech stimuli. EURASIP J Appl
Signal Process. 2002;11(1):1165–73.
82. Sodoyer D, Girin L, Jutten C, Schwartz JL. Developing an audio-
visual speech source separation algorithm. Speech Commun.
2004;44(1–4):113–25.
83. Stewart C, Winfield L, Junt A, Bressman SB, Fahn S, Blitzer A,
Brin MF. Speech dysfunction in early Parkinson’s disease. Mov
Disord. 1995;10(5):562–5.
84. Sunderland T, Hill JL, Mellow AM, et al. Clock drawing in
Alzheimer’s disease: a novel measure of dementia severity. J Am
Geriatr Soc. 1989;37:725–9.
85. Trombetti A, Hars M, Herrmann FR, Kressig RW, Ferrari S,
Rizzoli R. Effect of music-based multitask training on gait, bal-
ance, and fall risk in elderly people: a randomized controlled trial.
Arch Intern Med. 2011;171(6):525–33.
86. Stone EE, Skubic M. Evaluation of an inexpensive depth camera
for passive in-home fall risk assessment. In: Proceedings of the
4th International conference on pervasive computing technolo-
gies for healthcare. Dublin; 2011.
87. Sumby WH, Pollack I. Visual contribution to speech intelligi-
bility in noise. J Acoust Soc Am. 1954;26(2):212–5.
88. Tripolitia EE, Fotiadisb DI, Argyropoulou M. A supervised
method to assist the diagnosis and monitor progression of Alz-
heimer’s disease using data from an fMRI experiment. Artif Intell
Med. 2011;53(1):35–45.
89. Tucha O, Mecklinger L, Thome J, Reiter A, Alders GL, Sartor H,
Naumann M, Lange KW. Kinematic analysis of dopaminergic
effects on skilled handwriting movements in Parkinson’s disease.
J Neural Transm. 2006;113:609–23.
90. Tucha O, Mecklinger L, Walitza S, Lange KW. The effect of
caffeine on handwriting movements in skilled writers. Hum Mov
Sci. 2006;25(4–5):523–35.
91. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait
markers and incident fall risk in older adults. J Gerontol Ser A
Biol Sci Med Sci. 2009;64A(8):896–901.
92. Vin
˜als Carrera F, Puente Balsells ML. ‘‘Grafologı
´a criminal’’,
capı
´tulo 3, alteraciones neurolo
´gicas y biolo
´gicas. Editorial
Herder, 2009.
93. Warkentin S, Erikson C, Janciauskiene S. rCBF pathology in
Alzheimer’s disease is associated with slow processing speed.
Neuropsychologia. 2008;46(5):1193–200.
94. Werner Perla, Rosenblum Sara, Bar-On Gady, Heinik J, Korczyn
A. Handwriting process variables discriminating mild Alzheimer’s
Cogn Comput
123
Author's personal copy
disease and mild cognitive impairment’’. J Gerontol. 2006;61B(4):
228–36.
95. Woodward J. Biometrics: identifying law and policy concerns. In:
Jain AK, Bolle RM, Pankanti S, editors. Biometrics: personal
identification in networked society. New York: Springer; 2005.
p. 385–405.
96. Zelkha E, Epstein B, Birrell S, Dodsworth C. From devices to
‘‘ambient intelligence’’. Digital Living Room Conference, June
1998. http://www.epstein.org/brian/ambient_intelligence/DLR%
20Final%20Internal.ppt.
Cogn Comput
123
Author's personal copy
