Mining Handwriting Images to Predict Alzheimer’s

Neurodegenerative diseases, such as Alzheimer and Parkinson’s, are distinguished by the progressive loss of motor capabilities, cognitive abilities, or both. The specific origins of neurodegenerative disorders are often unclear; however, they can include genetic, environmental, and lifestyle factors, and are scientifically interpreted as a breakdown of brain and spinal cord functioning. Treatment often focuses on symptom management, as there is presently no cure for these disorders, only treatments to delay disease progression and improve patients’ quality of life. An early diagnosis is crucial to initiate treatment promptly since significant and irreversible damage may have already occurred once symptoms manifest. It is widely acknowledged that one of the initial skills affected by cognitive disorders is handwriting, which relies on a combination of kinesthetic and motor-perceptive abilities. Significant progress has been achieved in this field, beginning with establishing various handwriting protocols that outline the specific writing or drawing tests to be conducted. Nevertheless, it’s important to highlight that there is no universal consensus regarding the quantity and nature of tasks to be utilized. Additionally, there is a scarcity of standardized databases that gather this kind of data, which typically only refers to a relatively small number of participants. This aspect adds another layer of complexity in the realm of machine learning techniques, which typically demand substantial volumes of data. Moreover, there is a lack of consensus regarding the specific features researchers should prioritize. Indeed, the challenge of identifying effective features that enable the system to differentiate between regular age-related handwriting changes and those induced by neurodegenerative disorders remains unresolved. This paper will investigate existing studies focusing on detecting Alzheimer’s disease using handwriting analysis. Our examination will encompass the databases employed, the features extracted, the methodologies applied, and the ultimate discoveries and conclusions drawn from these studies.