SynthesisSensors (Basel, Switzerland)2022
Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Wearable Sensors in Gait Assessment for Parkinson Disease and Stroke in Real-World Environments: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- The future digital outpatient clinic for movement disorders: from technological innovation to ethical and systemic implementation-a position paper.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- An Explainable Ensemble and Deep Learning Framework for Accurate and Interpretable Parkinson's Disease Detection from Voice Biomarkers.Diagnostics (Basel, Switzerland) · 2025Article
- Differential Effects of Levodopa and Stimulation on Post-Surgery Freezing of Gait in Subthalamic Nucleus Deep Brain Stimulation Parkinson's Disease Patients: A Clinical and Kinematic Analysis.Movement disorders clinical practice · 2025Article
- Assessing the Generalizability of Foundation Models for the Recognition of Motor Examinations in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Parkinson's Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation.Cells · 2025Review
- Opportunities and Limitations of Wrist-Worn Devices for Dyskinesia Detection in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.Journal of Parkinson's disease · 2025Article
- Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Enhanced Parkinson's disease prediction using LDEFS feature selection and Mamdani fuzzy neural network.Frontiers in aging neuroscience · 2025Article
- Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives.Frontiers in public health · 2025Article
- Soft Robotics for Parkinson's Disease Supported by Functional Materials and Artificial Intelligence.BME frontiers · 2025Review
- From Night to Light: A Bibliometric Analysis of the Global Research Trajectory of Sleep Disorders in Parkinson's Disease.Journal of multidisciplinary healthcare · 2025Article
- Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors.Sensors (Basel, Switzerland) · 2024Article
- A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson's Disease Rehabilitation.Sensors (Basel, Switzerland) · 2024Review
- Self-Management Systems for Patients and Clinicians in Parkinson Care: Protocol for an Integrated Scoping Review, Product Search, and Evaluation.JMIR research protocols · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
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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.