Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics

Diagnosing Parkinson’s disease (PD) promptly, accessibly and effectively is crucial for improving patient outcomes, yet reaching this goal remains a challenge. Here we developed a diagnostic pen featuring a soft magnetoelastic tip and ferrofluid ink, capable of sensitively and quantitatively converting both on-surface and in-air writing motions into high-fidelity, analyzable signals for self-powered PD diagnostics. The diagnostic pen’s working mechanism is based on the magnetoelastic effect in its magnetoelastic tip and the dynamic movement of the ferrofluid ink. To validate the clinical potential, a pilot human study was conducted, incorporating both patients with PD and healthy participants. The diagnostic pen accurately recorded handwriting signals, and a one-dimensional convolutional neural network-assisted analysis successfully distinguished patients with PD with an average accuracy of 96.22%. Our development of the diagnostic pen represents a low-cost, widely disseminable and reliable technology with the potential to improve PD diagnostics across large populations and resource-limited areas. This study presents a diagnostic pen with ferrofluid ink that converts handwriting into sensing signals for Parkinson’s disease (PD) diagnostics. In pilot studies, neural network-assisted analysis of collected handwriting signals accurately distinguished patients with PD, demonstrating the pen’s potential as a low-cost, scalable tool for accessible diagnostics.