A new algorithm for kinematic analysis of handwriting data; towards a reliable handwriting-based tool for early detection of alzheimer's disease

摘要

Early detection of Alzheimer's disease (AD) has attracted the attention of scientific and clinical community because of its application in control, early care, and treatment. The development of a cost-effective but reliable method is a challenge in this field. To address this challenge, in this study an effort was made to represent an efficient algorithm based on the analysis of handwriting data. Detection of premonitory symptoms using the handwriting data could be more difficult due to individual differences, effects of different sources of variability and noises. For this purpose, a noise-robustness paradigm was adopted that was independent of small variations. It was based on the singular value decomposition technique and sparse non-negative least-square classifier. To find out the best results, the effects of single and dual-task conditions as well as several handwriting time series such as horizontal, vertical and absolute velocity, acceleration, pressure, and trajectory curvature were studied.