JMIR Publications

ABSTRACT

Background:

Background:

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a rapidly growing global prevalence. Current clinical assessments, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), are limited by subjectivity and episodic application, creating a need for continuous, objective monitoring solutions.

Objective:

Objective:

This review synthesizes progress (2019–2024) in multimodal intelligent monitoring systems for PD, focusing on the quantification of motor and non-motor symptoms, algorithm development, and the clinical translation of remote monitoring platforms.

Methods:

Methods:

A targeted literature search was conducted in PubMed, Web of Science, and CNKI. Eligible studies were thematically analyzed across three domains: sensor-based symptom assessment, multimodal algorithms, and remote monitoring platforms. Methodological reporting followed PRISMA-ScR guidelines.

Results:

Results:

Wearable sensors demonstrated high concordance with clinical scores (e.g., 89% for tremor detection). Computer vision achieved moderate agreement with clinician ratings (ICC=0.74 for bradykinesia). Remote platforms improved medication adherence (up to 85%) and reduced outpatient visits (by 29% in one study). A heuristic CPT-PD framework was proposed to integrate key components of diagnosis, treatment, and management.

Conclusions:

Conclusions:

Multimodal intelligent monitoring enhances objectivity and continuity in PD assessment and shows promising clinical utility. Persistent challenges include fragmented symptom focus, algorithmic heterogeneity, and barriers to adoption among older adults. Future efforts should prioritize integrated, patient-centered ecosystems to enable precision management of PD.