A Brief Overview on Handwriting Analysis for Neurodegenerative Disease Diagnosys

Abstract

Degenerative nerve diseases a ect many of your body's activities, 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 e ective tool for early diagnoses for this kind of diseases. Moreover, as they show di erent 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 analysis for early diagnosis, monitoring and tracking of neurodegenerative diseases. In particular, we taken into account Alzheimer and Parkinson diseases.

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Neurodegenerative diseases (NGD) a ect 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 identi cation 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, kinesthetic, and perceptual-motor abilities [ 28 ]. Furthermore, visual and kinesthetic perception, motor planning, eye-hand coordination, visual-motor integration, dexterity, and manual skills are involved. Signi cant changes of the handwriting performances are a prominent feature of Alzheimer Disease (AD) as well as

Parkinson Disease (PD). Learning and performing handwriting requires the interactions 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 di erent approaches, such as neuroimaging and intracranial recordings, clinical treatments, and proposed neural schemes aimed at evaluating the viability of theories regarding the way these areas cooperate 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 eld of application is also encouraged by the fact that the Minnesota Handwriting Assessment (MHA) is used to identify students (6-8 years old) with di culties related to autism. The test is also used to evaluate treatment e ectiveness 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; nally, Section 4 is devoted to the conclusions. 2

State of the Art

As mentioned in the Introduction, handwriting involves several cognitive abilities. For this reason, handwriting analysis can be used as an e ective tool for early diagnoses for NGD [ 14, 23 ]. Moreover, since this kind of diseases show different 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 processes 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 interactions during attentive imitation and predictive learning of sequential handwriting 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 correlationbased learning model of the cerebellum was proposed suggesting that basal ganglia 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 simpli ed level of biological abstraction [ 2 ]. To develop e ective and e cient systems for the early detection and monitoring of NGD by Handwriting analysis, de ning e ective features plays a key role. For this reason, new methodologies of features extraction and classi cation have been proposed, taking 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.

Signi cant handwriting di culties were already reported by Alois Alzheimer when describing the rst 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 impairment (MCI) compared with those with mild Alzheimers disease and healthy controls; the aim was to assess the importance of measures for the di erentiation of the groups and to assess the characteristics of the handwriting process across di erent 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 di erences in ne 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 ne motor performance and the movements of AD patients were signi cantly less regular than those of healthy controls. Recently, also handwritten signatures 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 e ective. In the study presented in [32], the patients performed four types of handwriting movements on a digitizer. Movement 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 pro les 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 a ects 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 monitoring 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 rst signs of impending PD. Previous research has shown that handwriting measures have the potential for identifying various stages of PD, e ects of varied interventions [ 4 ] and the e ect of medication [ 22 ]. Moreover, studies focusing on understanding the mechanism underlying micrographia found signi cant di erences between the handwriting of PD patients and healthy subjects[ 27, 29 ]. More recently, further studies have been conducted to analyze the handwriting of patients a ected by PD [ 23, 11, 26, 3 ]. In [ 23 ] the authors try to identify simple characteristics of handwriting which could accurately di erentiate PD patients from healthy controls. Patients were asked to write their name and to copy an address on a paper a xed 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 con rmed that these routine writing tasks can be used to di erentiate PD patients from healthy controls. Letanneux et al. [ 11 ] identi ed several studies that investigated handwriting in PD, either with conventional pencil-and-paper measures or with graphic tablets, and reported their ndings on key spatiotemporal and kinematic variables. They found that kinematic variables (velocity, uency) di erentiate better between control participants and PD patients, and between o - and on-treatment PD patients, than the traditional measure of static writing size. Moreover, since handwriting de cit for PD patients is not restricted to micrographia, they propose the term "PD dysgraphia", which encompasses all de cits 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. Finally, Drotar 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 classi ers: K-nearest neighbors, ensemble AdaBoost classi er, 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 di erent 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 e ort has been done in order to get an acceptable dimension (55 individual) [ 16 ]. { Acquisition tool and protocol: in many cases o -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 a ect 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 acquisition 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 di erent stages, a set of di erent 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 speci c event is occurred.

The second issue is that to face the classi cation problem. Often standard Signal Processing and Pattern Recognition techniques are applied with very few cases of specialization to the eld. The main problem is that the medical knowledge 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 reduced usefulness in real word. From this perspective the challenge is to identify patients at di erent 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 Recognition approaches should be speci cally 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 eld that must be addressed.

Handwriting analysis is an e ective 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 e ective; (ii) since handwriting kinematics has shown to be useful for discriminating between patients and healthy controls, new protocols for the on-line acquisition of handwriting should be de ned; (iii) de ning pattern recognition tools speci cally 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. 29. Tucha, O., Mecklinger, L., Thome, J., Reiter, A., Alders, G., Sartor, H., Naumann, M., Lange, K.: Kinematic analysis of dopaminergic e ects on skilled handwriting movements in parkinson's disease. Journal of Neural Transmission 113(5), 609{623 (2006) 30. Wan, J., Byrne, C.A., OGrady, M.J., OHare, G.M.P.: Managing wandering risk in people with dementia. IEEE Transactions on Human-Machine Systems 45(6), 819{823 (Dec 2015) 31. Werner, P., Rosenblum, S., Bar-On, G., Heinik, J., Korczyn, A.: Handwriting process variables discriminating mild alzheimer's disease and mild cognitive impairment. Journal of Gerontology: PSYCHOLOGICAL SCIENCES 61(4), 228{36 (2006) 32. Yan, J.H., Rountree, S., Massman, P., Doody, R.S., Li, H.: Alzheimers disease and mild cognitive impairment deteriorate ne movement control. Journal of Psychiatric Research 42(14), 1203{1212 (2008) 33. Yasuda, K., Nakamura, T., Beckman, B.: Brain processing of proper names. Aphasiology 14(11), 1067{1089 (2000)

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