=Paper=
{{Paper
|id=Vol-1982/paper2
|storemode=property
|title=A Brief Overview on Handwriting Analysis for Neurodegenerative Disease Diagnosis
|pdfUrl=https://ceur-ws.org/Vol-1982/paper2.pdf
|volume=Vol-1982
|authors=Claudio De Stefano,Francesco Fontanella,Donato Impedovo,Giuseppe Pirlo,Alessandra Scotto di Freca
|dblpUrl=https://dblp.org/rec/conf/aiia/StefanoFIPF17
}}
==A Brief Overview on Handwriting Analysis for Neurodegenerative Disease Diagnosis==
https://ceur-ws.org/Vol-1982/paper2.pdf
A Brief Overview on Handwriting Analysis for
Neurodegenerative Disease Diagnosys
C. De Stefano1 , F. Fontanella1 , D. Impedovo2 , G. Pirlo2 and A. Scotto di
Freca1
1
Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI)
Università di Cassino e del Lazio meridionale
Via G. Di Biasio, 43 02043 Cassino (FR) – Italy
{destefano,fontanella,a.scotto}@unicas.it
2
Dipartimento di Informatica
Università di Bari (Italy)
{donato.impedovo,giuseppe.pirlo}@uniba.it
Abstract. Degenerative nerve diseases affect many of your body’s activ-
ities, such as balance, movement, talking, breathing, and heart function.
These disease cannot be cured, nonetheless an early diagnosis can help
to better manage the symptoms and the evolution of these diseases.
Since handwriting involves several cognitive abilities, clinicians started
to consider handwriting analysis as an effective tool for early diagnoses
for this kind of diseases. Moreover, as they show different handwriting
impairments as they evolve, handwriting analysis can be also used for
monitoring them along the clinical course.
This paper provides a brief overview on the use of handwriting anal-
ysis for early diagnosis, monitoring and tracking of neurodegenerative
diseases. In particular, we taken into account Alzheimer and Parkinson
diseases.
1 Introduction
Neurodegenerative diseases (NGD) affect the peripheral nervous system which
includes muscles, the nerve-muscle junction, nerves in the limbs, and motor nerve
cells in the spinal cord. Nerve cells send the messages that control these muscles
in order to allow movements, including handwriting. Sick/died neurons cannot
properly control muscles. These diseases are incurable, but early screening and
identification can reduce the ”diagnostic odyssey”. On the other hand NMD often
result in progressive cognitive, functional and behavioural changes. The current
clinical diagnostic tools include imaging (e.g. magnetic resonance imaging, or
MRI), blood tests, lumbar puncture (spinal tap).
Handwriting results from a complex network composed by cognitive, kines-
thetic, and perceptual-motor abilities [28]. Furthermore, visual and kinesthetic
perception, motor planning, eye-hand coordination, visual-motor integration,
dexterity, and manual skills are involved. Significant changes of the handwrit-
ing performances are a prominent feature of Alzheimer Disease (AD) as well as
D. Impedovo and G. Pirlo (Eds.), Workshop on Artificial Intelligence with Application in Health, Bari, Italy, November 14, 2017.
Copyright held by the authors.
Parkinson Disease (PD). Learning and performing handwriting requires the in-
teractions of multiple brain areas, comprising cerebral cortex, basal ganglia and
cerebellum [10]. In seeking to understand all the breadth and facets of motor
learning, many researchers have used different approaches, such as neuroimag-
ing and intracranial recordings, clinical treatments, and proposed neural schemes
aimed at evaluating the viability of theories regarding the way these areas co-
operate for motor learning/generation. One of the earliest neural schemes for
motor control, which envisages the cooperation among cortex, cerebellum and
basal ganglia, was proposed in 1974 [1]. On the other hand, the perspective of
signal and image processing must be considered: there are certain aspects of the
writing process that are more vulnerable than other and may present diagnostic
signs [13]. Dysgraphia has been observed in patients presenting mild to moderate
levels AD [14] and PD [16].
The idea of handwriting analysis within this field of application is also en-
couraged by the fact that the Minnesota Handwriting Assessment (MHA) is used
to identify students (6-8 years old) with difficulties related to autism. The test is
also used to evaluate treatment effectiveness over time. It inspects the legibility,
handwriting speed, legibility, form, alignment, size and spacing. The MHA is a
standard in US, it requires 10 minutes to be performed and it costs less than
130$. This last aspect opens for a real possibility of having, in future, a similar
NGD assessment.
The paper is organized as follows: Section 2 describes the state of the art
for early detection and monitoring of NGD by handwriting analysis; Section 3
illustrates the open issues that still need to be addressed; finally, Section 4 is
devoted to the conclusions.
2 State of the Art
As mentioned in the Introduction, handwriting involves several cognitive abil-
ities. For this reason, handwriting analysis can be used as an effective tool for
early diagnoses for NGD [14, 23]. Moreover, since this kind of diseases show dif-
ferent handwriting impairments as they evolve, handwriting analysis can be also
used for monitoring them along the clinical course [8, 25, 33]. Studies involving
neural recordings have provided a large body of knowledge about the neural pro-
cesses occurring in the brain areas related to motor learning. First studies on the
brain areas governing handwriting observed that it implies the learning of motor
sequences by two distinct neural systems, comprising cortex-basal ganglia and
cortex-cerebellum loop circuits [12, 6]. A neural model of cortico-cerebellar in-
teractions during attentive imitation and predictive learning of sequential hand-
writing movements, suggests how cortical mechanisms interact with predictive
cerebellar learning during movement imitation [5]. Recently a recurrent neural
network actor-critic model of the basal ganglia and a feed-forward correlation-
based learning model of the cerebellum was proposed suggesting that basal gan-
glia and cerebellar learning systems work in parallel and interact with each other.
However, these works did not provide computational models to test the validity
of the neural schemes or comprised only basal ganglia [6] or cerebellum [5], or
were built with a simplified level of biological abstraction [2]. To develop effective
and efficient systems for the early detection and monitoring of NGD by Hand-
writing analysis, defining effective features plays a key role. For this reason, new
methodologies of features extraction and classification have been proposed, tak-
ing into account both image processing techniques and writing generation model
techniques. In particular, it has been observed that the use of Sigma-Lognormal
[15] and Delta-Log [18] models can be adopted to generate features representing
the strokes [9]. These models have been developed from the kinematics theory
of rapid human movements [19, 20] and in[9] the authors presented a system for
handwriting analysis to investigate insurgence and monitoring of the Alzheimer’s
disease.
Significant handwriting difficulties were already reported by Alois Alzheimer
when describing the first patient with Alzheimer’s Disease (AD) in 1907. He
observed that the patient reduplicated the same syllable and forgot some others.
The evolution of agraphic impairments in AD was described in [21] and included
lexicosemantic disturbances at the beginning of the disease, with impairments
becoming more and more phonological as the dementia becomes more severe.
More recently, several studies analyzed the dynamic of the handwriting process
in order to detect and monitor AD [31, 9, 24, 17, 32]. In [31] the authors performs
kinematic measures of the handwriting process of persons with mild cognitive im-
pairment (MCI) compared with those with mild Alzheimers disease and healthy
controls; the aim was to assess the importance of measures for the differentiation
of the groups and to assess the characteristics of the handwriting process across
different functional tasks. Impedovo D et al. [9] use the Delta-Log and Sigma-Log
models mentioned above to investigate on the handwriting generation processes
and present a computational system to investigate insurgence and monitoring of
AD. In [24] the authors analyze handwriting kinematic to quantify differences
in fine hand motor function in patients with probable AD and mild cognitive
impairment compared to depressed patients and healthy controls. The authors
found that both patients with MCI and patients with probable AD exhibited loss
of fine motor performance and the movements of AD patients were significantly
less regular than those of healthy controls. Recently, also handwritten signa-
tures have been investigated for early diagnosis of NGD [17]. Pirlo et al. used
the sigma-lognormal model for the signature representation, then they analyzed
the health condition of the signer in terms of Alzheimer disease. The proposed
approach has shown to be cheap and effective. In the study presented in [32], the
patients performed four types of handwriting movements on a digitizer. Move-
ment time and smoothness were analyzed between the groups of patients take
into account (probable AD, MCI and normal controls) and across the movement
patterns. Kinematic profiles were also compared among the groups. AD and MCI
patients demonstrated slower, less smooth, less coordinated, and less consistent
handwriting movements than their healthy counterparts.
Parkinson’s disease (PD) is a long-term degenerative disorder of the central
nervous system that mainly affects the motor system. Even if, to date, clinical
assessment remains the gold standard in the diagnosis of Parkinsons disease,
many studies have been proposed that use handwriting for detecting and moni-
toring PD, since abnormal handwriting is a well recognized manifestation of PD,
with micrographia being characteristic [7]. Handwriting anomalies may appear
years at the early stages of the disease and thus may be one of the first signs of
impending PD. Previous research has shown that handwriting measures have the
potential for identifying various stages of PD, effects of varied interventions [4]
and the effect of medication [22]. Moreover, studies focusing on understanding
the mechanism underlying micrographia found significant differences between
the handwriting of PD patients and healthy subjects[27, 29]. More recently, fur-
ther studies have been conducted to analyze the handwriting of patients affected
by PD [23, 11, 26, 3]. In [23] the authors try to identify simple characteristics of
handwriting which could accurately differentiate PD patients from healthy con-
trols. Patients were asked to write their name and to copy an address on a
paper affixed to a digitizer. Mean pressure and mean velocity was measured
for the entire task and the spatial and temporal characteristics were measured
for each stroke. The experimental results confirmed that these routine writing
tasks can be used to differentiate PD patients from healthy controls. Letanneux
et al. [11] identified several studies that investigated handwriting in PD, either
with conventional pencil-and-paper measures or with graphic tablets, and re-
ported their findings on key spatiotemporal and kinematic variables. They found
that kinematic variables (velocity, fluency) differentiate better between control
participants and PD patients, and between off- and on-treatment PD patients,
than the traditional measure of static writing size. Moreover, since handwriting
deficit for PD patients is not restricted to micrographia, they propose the term
”PD dysgraphia”, which encompasses all deficits characteristic of Parkinsonian
handwriting. In [26] in order to assess whether standardized handwriting can
provide quantitative measures to distinguish PD patients from healthy controls,
the authors recorded pen tip trajectories during circle, spiral and line drawing
and repeated character ’elelelel’ and sentence writing. The experimental results
show that these tasks can provide objective measures for bradykinesia, tremor
and micrographia to distinguish Parkinson patients from healthy controls. Fi-
nally, Drotár et al. in [3], present a novel PD handwriting database consisting
of handwriting samples from (PD) patients and healthy controls. Each sample
contains kinematic and pressure data of height handwriting tasks. The tasks
include drawing an Archimedean spiral, repetitively writing orthographically
simple syllables and words, and writing of a sentence. To discriminate between
PD patients and healthy subjects, the authors use three well known and widely
used classifiers: K-nearest neighbors, ensemble AdaBoost classifier, and support
vector machines (SVM), which was the best performing one.
3 Open Issues
Although some research has been already carried out and some encouraging
result has been observed, there are still many open issues that must be addressed.
First of all there is the lack of a well designed dataset [30]. This involves many
different aspects:
– Cardinality of the set: in fact even considering papers dealing with PD,
most of them make use of datasets composed by very few subjects. More
recently some effort has been done in order to get an acceptable dimension
(55 individual) [16].
– Acquisition tool and protocol: in many cases off-line acquisition has been
performed due to the availability of handwritten document, however it must
be considered that on-line acquisition is able to provide a wide set of useful
dynamics. On the other hand, the choice of the acquisition tool also affect the
amount of dynamics that can be taken into account (e.g. pen-based camera,
pad, frontal video, etc.).
– Cognitive model: as already mentioned, neurodegenerative diseases do not
involve only functional and behavioural changes (can be encountered within
the handwriting), but also result in progressive cognitive decay. The acqui-
sition protocol should take into account, to some extent, also this aspect in
order to be able to convey as much information as possible.
– Number and periodicity of sessions: in order to be able to identify disease
at different stages, a set of different users is needed. At the same time in
order to have the possibility to understand the evolution of the disease over
time, the same patient must be enrolled into the system in a periodic way,
or when some specific event is occurred.
The second issue is that to face the classification problem. Often standard
Signal Processing and Pattern Recognition techniques are applied with very few
cases of specialization to the field. The main problem is that the medical knowl-
edge of the evolution of the disease cannot be ignored: an automatic system
able to distinguish an healthy person and a late-stage sick one has a very re-
duced usefulness in real word. From this perspective the challenge is to identify
patients at different stages, tracking the evolution and to understand/identify
signs of worsening. It must be underlined that today there is no cure for AD but
it can only be somehow managed, so that an early diagnosis and follow up may
have profound implications for carers and doctors. Research on handwriting and
neuro-muscular diseases is not expected to replace standard techniques, but to
strengthen them by allowing an earlier diagnosis. To this aim Pattern Recogni-
tion approaches should be specifically studied and coupled with Cognitive and
neuro-muscular generation models.
4 Conclusions
In this paper we propose a brief overview of the handwriting analysis approaches
for early diagnosis, monitoring and tracking of neurodegenerative diseases. In
particular we taken into account Alzheimer and Parkinson diseases. Furthermore,
we also discuss the still open issues in the field that must be addressed.
Handwriting analysis is an effective tool for dealing with the diagnosis and
monitoring of the above cited diseases, nonetheless some issues are open, and are
mainly related to: (i) because of their cardinalities, most of the datasets currently
available does not allow pattern recognition tools to be effective; (ii) since hand-
writing kinematics has shown to be useful for discriminating between patients
and healthy controls, new protocols for the on-line acquisition of handwriting
should be defined; (iii) defining pattern recognition tools specifically devised for
the automatic diagnosis and monitoring of neurodegenerative diseases.
Acknowledgment
This work is supported by the Italian Ministry of Education, University and
Research (MIUR) within the PRIN2015-HAND project.
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