# Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review
## 4 Conclusions and Future Directions  
The body of evidence on computerized handwriting analysis supports the hypothesis that physical, cognitive, and psychological characteristics of individuals can be captured by handwriting measures. In particular, changes in handwriting seem to be a prominent _biomarker_ for the evaluation of neurodegenerative diseases. Several works, in fact, provided evidence that the automatic discrimination between unhealthy and healthy people can be accomplished on the basis of features obtained through simple and easy-to-perform handwriting tasks. In this view, as the number of devices for data capturing and processing is increasing all over the world, the use of handwriting to detect and monitor health conditions is becoming more and more attractive. In particular, the advent of digitizing tablets and electronic pens allow researchers to investigate not only the static characteristics of handwriting available only after the writing process has occurred but also dynamic characteristics collected when the handwriting task is still in progress. Based on dynamic analyses, several works provided evidence that certain aspects of the handwriting process are more vulnerable than others and may therefore present diagnostic signs. A handwriting-based decision support system has the potential to assist clinicians at the point of care, providing a novel diagnostic tool while reducing the expenditure of public health care. Moreover, it can be used to quantify aspects of the motor system and its disorders in order to better understand the involved underlying mechanisms, e.g., the difficulties in coordinating the components of a motor sequence movement. Finally, it can help study the effects of medication on handwriting with the aim to monitor the responsiveness of the patient to therapy. More in general, handwriting can provide a simple, user-friendly, and easy-to-use instrument to support the daily clinical trials. Artificial intelligence and machine learning, in fact, are changing the way we think of health care from many perspectives, and the use of computer aided tools within medical practice is continuously increasing. The best perspective of this line of research is the integration of new medical tools which can increase the level of diagnostic accuracy. Doctors can be provided with user-friendly tools in their daily practice, even though they are not necessarily skilled in high-level algorithms. In this sense, a handwriting-based tool is attractive since it not only provides the user with an automatic response within few seconds but also allows the doctor to store useful metadata, e.g., the patient's information and diagnosis, for later use. Of course, handwriting-based intelligent systems are not expected to replace standard techniques or even doctors but rather to provide additional evidence to further support the clinical assessment. Intelligent systems technology is proving beneficial in a number of health domains, also including neuroimaging [111] and cardiovascular risk assessment [112]. The present paper has been devoted to provide a comprehensive overview of the literature dealing with the application of dynamic handwriting analysis to the assessment of Parkinson's disease and Alzheimer's disease. Three well-defined research trends have been identified, ranging from studies aimed at understanding the facets of fine motor control related to the disease to works investigating the application of dynamic handwriting features to disease monitoring and diagnosis. Handwriting features in PD and AD patients can overlap; in particular, handwriting is typically slower, less regular, and less consistent in patients if compared to the healthy counterpart. However, the two diseases also show distinctive characteristics. PD patients tend to exhibit rigidity of movements and unwanted muscle contractions, while preserving, in most cases, their cognitive faculties.