Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits

Introduction: Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched withmotion and force sensors. Methods: Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group di erences and correlations with clinical scores. We applied machine learning classificationmodels to test the indicators ability to discriminate between groups, with a focus on model interpretability. Results: Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlatemoderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important. Conclusion: Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.

Automated summary

This study presents a novel smart ink pen for spiral drawing assessment, aiming to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors. Machine learning classification models were applied to test the indicators' ability to discriminate between Parkinsonian patients and age-matched controls. The results showed that the indicators were able to significantly identify Parkinson's disease motor symptoms, supporting the introduction of the smart ink pen as a time-efficient tool to complement clinical assessment.