Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring
Abstract
1. Introduction
2. Handwriting Impairments in Parkinson’s Disease
2.1. Micrographia: Definition and Characteristics
2.2. Kinematic and Dynamic Features Affected by PD
2.3. Traditional Methods for Analyzing Handwriting in PD
3. Artificial Intelligence and Its Role in Healthcare
3.1. Definition and Core Concepts of AI
3.2. Machine Learning, Deep Learning, and Pattern Recognition
3.3. AI Applications in Neurology and Movement Disorders
4. AI-Based Handwriting Analysis in Parkinson’s Disease
4.1. Data Collection Methods: Paper, Digital Tablets, and Smartpens
4.2. Key Features Extracted: Spatial, Temporal, and Kinematic Parameters
4.3. Algorithms and Techniques: Supervised Learning Models and Deep Learning Architectures (CNNs, LSTMs)
4.4. Hybrid Approaches Combining Signal Processing and AI
5. Clinical Applications and Research Findings
5.1. Early Diagnosis and Screening
5.2. Disease Progression Monitoring
5.3. Treatment Response Evaluation
6. Advantages and Limitations of AI-Based Handwriting Analysis
6.1. Non-Invasive and Cost-Effective Monitoring
6.2. Challenges in Data Quality and Standardization
6.3. Ethical Considerations and Patient Privacy
7. Ongoing Research
7.1. Integration with Wearable Devices and Smart Environments
7.2. Multimodal Data Fusion (Handwriting + Speech + Gait Analysis)
7.3. Real-World Deployment, Telemedicine Integration, and Non-Invasive Brain Stimulation
8. Conclusions
9. Final Reflections and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Feature Type | Examples |
---|---|---|
Conventional features [54] | Temporal | Writing duration, stroke duration |
Spatial | Stroke width, height, and length | |
Kinematic | Velocity, acceleration, jerk | |
Dynamic | Pressure, tilt, azimuth | |
Advanced features [54] | Entropy measures | Assessing randomness and irregularity of fine movements |
Signal-to-noise ratio (SNR) | Evaluating motor signal clarity via signal-to-noise ratio | |
Empirical mode decomposition (EMD) | Decomposing signals into intrinsic mode functions | |
Cepstrum analysis [103] | Identifying periodic patterns | |
Sigma-lognormal models [104] | Neuromotor-based modeling of stroke velocity |
Device | Short Description |
---|---|
Parkinson’s KinetiGraph (PKG™) | Wrist-worn monitor for bradykinesia and dyskinesia episodes. |
Kinesia 360™ | Wrist-and-ankle sensor system linked to a smartphone app for tremor and movement tracking. |
KinesiaU™ | Smartwatch-based symptom tracker for patient self-monitoring via the Kinesia app. |
PDMonitor® | Multi-sensor network (wearable on trunk and limbs) capturing gait, tremor, on/off fluctuations, etc. |
STAT-ON™ [19] | Belt-worn inertial recorder logging on/off states, dyskinesia, falls, and gait parameters. |
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Marano, G.; Rossi, S.; Marzo, E.M.; Ronsisvalle, A.; Artuso, L.; Traversi, G.; Pallotti, A.; Bove, F.; Piano, C.; Bentivoglio, A.R.; et al. Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring. Biomedicines 2025, 13, 1764. https://doi.org/10.3390/biomedicines13071764
Marano G, Rossi S, Marzo EM, Ronsisvalle A, Artuso L, Traversi G, Pallotti A, Bove F, Piano C, Bentivoglio AR, et al. Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring. Biomedicines. 2025; 13(7):1764. https://doi.org/10.3390/biomedicines13071764
Chicago/Turabian StyleMarano, Giuseppe, Sara Rossi, Ester Maria Marzo, Alice Ronsisvalle, Laura Artuso, Gianandrea Traversi, Antonio Pallotti, Francesco Bove, Carla Piano, Anna Rita Bentivoglio, and et al. 2025. "Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring" Biomedicines 13, no. 7: 1764. https://doi.org/10.3390/biomedicines13071764
APA StyleMarano, G., Rossi, S., Marzo, E. M., Ronsisvalle, A., Artuso, L., Traversi, G., Pallotti, A., Bove, F., Piano, C., Bentivoglio, A. R., Sani, G., & Mazza, M., on behalf of Lazio DBS Study Group. (2025). Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson’s Disease Diagnosis and Monitoring. Biomedicines, 13(7), 1764. https://doi.org/10.3390/biomedicines13071764