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Objective: This study aimed to evaluate lenition, a phonological process involving consonant weakening, as a diagnostic marker for differentiating Parkinson’s Disease (PD) from Atypical Parkinsonism (APD). Early diagnosis is critical for optimizing treatment outcomes, and lenition patterns in stop consonants may provide valuable insights into the distinct motor speech impairments associated with these conditions. Methods: Using Phonet, a machine learning model trained to detect phonological features, we analyzed the posterior probabilities of continuant and sonorant features from the speech of 142 participants (108 PD, 34 APD). Lenition was quantified based on deviations from expected values, and linear mixed-effects models were applied to compare phonological patterns between the two groups. Results: PD patients exhibited more stable articulatory patterns, particularly in preserving the contrast between voiced and voiceless stops. In contrast, APD patients showed greater lenition, particularly in voiceless stops, coupled with increased articulatory variability, reflecting a more generalized motor deficit. Conclusions: Lenition patterns, especially in voiceless stops, may serve as non-invasive markers for distinguishing PD from APD. These findings suggest potential applications in early diagnosis and tracking disease progression. Future research should expand the analysis to include a broader range of phonological features and contexts to improve diagnostic accuracy.
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This content will become publicly available on April 4, 2026
Diagnosing Parkinson’s disease via behavioral biometrics of keystroke dynamics
Parkinson’s disease (PD) is one of the rapidly growing neurodegenerative diseases, affecting more than 10 million people worldwide. Early and accurate diagnosis of PD is highly desirable for therapeutic interventions but remains a substantial challenge. We developed a soft, portable intelligent keyboard leveraging magnetoelasticity to detect subtle pressure variations in keystroke dynamics by converting continuous keystrokes into high-fidelity electrical signals, thus enabling the quantitative analysis of PD motor symptoms using machine learning. Relying on a fundamental working mechanism, the intelligent keyboard demonstrates highly sensitive, intrinsically waterproof, and biocompatible properties, with the successful demonstration in a pilot study on patients with PD. To facilitate the potential continuous monitoring of PD, a customized cellphone application was developed to integrate the intelligent keyboard into a wireless platform. Together, the intelligent keyboard system’s compelling properties position it as a promising tool for advancing early diagnosis and facilitating personalized, predictive, preventative, and participatory approaches to PD healthcare.
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- Award ID(s):
- 2425858
- PAR ID:
- 10587933
- Publisher / Repository:
- American Association for the Advancement of Science
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 11
- Issue:
- 14
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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