ArticleBiosensors2022
Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- A comprehensive survey on diagnosis and assessment of Parkinson's disease via plantar pressure analysis.NPJ Parkinson's disease · 2026Article
- Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.Journal of medical Internet research · 2026Article
- Pharmacists in modern primary care and management of diseases: an evidence-based perspective on emerging technologies.BioImpacts : BI · 2026Article
- A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.Sensors (Basel, Switzerland) · 2024Article
- Review
- The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review.Sensors (Basel, Switzerland) · 2023Review
- A Review of Artificial Intelligence-Based Gait Evaluation and Rehabilitation in Parkinson's Disease.Cureus · 2023Review
- CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments.Bioengineering (Basel, Switzerland) · 2023Article
- A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022.Frontiers in robotics and AI · 2023Article
- A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases.Biosensors · 2022Review
- Review
- Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of "Dry" Immersion.Sensors (Basel, Switzerland) · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective is the monitoring and assessment of gait in PD patients. We propose a wearable physiograph for qualitative and quantitative gait assessment, which performs bilateral tracking of the foot biomechanics and unilateral tracking of arm balance. Gait patterns are assessed by means of correlation. The surface plot of a correlation coefficient matrix, generated from the recorded signals, is classified using convolutional neural networks into physiological or PD-specific gait. The novelty is given by the proposed AI-based decisional support procedure for gait assessment. A proof of concept of the proposed physiograph is validated in a clinical environment on five patients and five healthy controls, proving to be a feasible solution for ubiquitous gait monitoring and assessment in PD. PD management demonstrates the complexity of the human body. A platform empowering multidisciplinary, AI-evidence-based decision support assessments for optimal dosing between drug and non-drug therapy could lay the foundation for affordable precision medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.