Multifunctional hydrogel sensors with dynamic covalent networks for machine learning-assisted Parkinson's disease diagnosis and encrypted human-computer interaction: An Evidence Synthesis on Scalability And Operational Maintainability

This evidence-synthesis article examines intelligent biomedical sensing through the focal contribution “Multifunctional hydrogel sensors with dynamic covalent networks for machine learning-assisted Parkinson's disease diagnosis and encrypted human-computer interaction” and nine author-disjoint, topically matched studies. The analysis is organized around scalability and operational maintainability. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

1. Ding, S., Yu, X., Wang, Q., Luo, P., Li, H., Li, Z., Wang, R., Liu, H., He, Y., Nong, J., & Zhang, C. (2025). Multifunctional hydrogel sensors with dynamic covalent networks for machine learning-assisted Parkinson's disease diagnosis and encrypted human-computer interaction. Materials Today Bio, 35, 102524. https://doi.org/10.1016/j.mtbio.2025.102524 DOI
2. Zhang, Z. (2026). Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring. Gels, 12(5), 449. https://doi.org/10.3390/gels12050449 DOI
3. Yan, C., Jiang, S., Wang, Y., Deng, J., Wang, X., Chen, Z., Chen, T., Huang, H., & Wu, H. (2024). A Wearable Sign Language Translation Device Utilizing Silicone-Hydrogel Hybrid Triboelectric Sensor Arrays and Machine Learning. . https://doi.org/10.2139/ssrn.4956060 DOI
4. Lin, J. (2025). Time-to-Fall Prediction in Parkinson’s Disease Using Wearable Sensor Data and Machine Learning. . https://doi.org/10.21203/rs.3.rs-6663222/v1 DOI
5. Liu, R., Zhuang, X., Lin, P., Lin, L., Liu, J., Hu, Y., You, R., & Lu, Y. (2026). A hydrogel-based SERS sensor with wearable potential and machine learning integration for sweat stimulant detection. Chemical Engineering Journal, 527, 171522. https://doi.org/10.1016/j.cej.2025.171522 DOI
6. Xu, K., & Wang, C. (2024). Recent Progress on Wearable Sensor based on Nanocomposite Hydrogel. Current Nanoscience, 20(2), 132-145. https://doi.org/10.2174/1573413719666230217141149 DOI
7. Thacker, C., Vivekanandhan, G., Veluvali, P., & Ramkumar, K. (2025). Wearable Sensor-Driven Stress Classification Utilizing Machine Learning Techniques. Proceedings of the 1st International Conference on Intelligent Methods and Advanced Computer Scientific Innovations, 655-661. https://doi.org/10.5220/0014205700004932 DOI
8. Santos, S., Sousa, J., & Ferreira, J. (2025). Wearable Electrodermal Activity Sensor for Real-Time Stress Detection Using Machine Learning. Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies, 188-196. https://doi.org/10.5220/0013257900003911 DOI
9. Verstraete, M., Conditt, M., & Goodchild, G. (2019). 0886 - Wearable Sensor Technology and Machine Learning as a Tool for Assessing Patient Recovery. . https://doi.org/10.26226/morressier.5c8f9096b5d368000a26b8bc DOI
10. Sinhal, A., Sinhal, A., & Sinhal, A. (2025). Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data. . https://doi.org/10.2139/ssrn.5346661 DOI