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

The discriminant capability of the proposed method was studied in 13 subjects with mild cognitive impairment, 15 with AD, and 15 healthy participants. They performed four writing tasks under single and dual task conditions. The new feature extracted from vertical acceleration yielded high average accuracy rates of 100% in classification between healthy controls and subjects with MCI. The average accuracy rate of 93.5% was also obtained in discriminating between healthy controls and AD patients. More investigations confirmed that using the proposed features under dual-task condition could enhance the detection rate. Achieving high performance using a relatively simple and cost-effective method demonstrated that it could potentially be used in clinical devices.