A team at the University of California, Los Angeles has developed a low-cost diagnostic pen that converts handwriting into electrical signals for early detection of Parkinson’s disease, achieving 96.22% accuracy in a pilot study.

Parkinson’s disease impairs the motor system, leading to tremors, stiffness, and slowed movements that impair fine motor functions such as handwriting. Clinical diagnosis today largely relies on subjective observations, which are prone to inconsistency and often inaccessible in low-resource settings. Biomarker-based diagnostics, while objective, remain constrained by cost and technical complexity.

In the study, “Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics,” published in Nature Chemical Engineering, researchers engineered a diagnostic pen to capture real-time motor signals during handwriting and convert them into quantifiable electrical outputs for disease classification.