ArticleDiagnostics (Basel, Switzerland)2020
A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson's Disease Using Wearable Based Gait Signals.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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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
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Concurrent Validity Evidence for Pressure-Sensing Walkways Measuring Spatiotemporal Features of Gait: A Systematic Review and Meta-Analysis.Sensors (Basel, Switzerland) · 2024Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges.Sensors (Basel, Switzerland) · 2021Pooled it
- Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026Review
- Systematic review of wearables assessing medication effect on motor function and symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.Scientific reports · 2025Article
- Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism.Biomedicines · 2025Article
- Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.Sensors (Basel, Switzerland) · 2024Article
- Article
- Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions.Diagnostics (Basel, Switzerland) · 2022Article
- Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring.Sensors (Basel, Switzerland) · 2022Article
- The Effect of Submaximal Exercise on Jugular Venous Pulse Assessed by a Wearable Cervical Plethysmography System.Diagnostics (Basel, Switzerland) · 2022Article
- A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.Metabolites · 2022Review
- Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization.Biosensors · 2022Article
- Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.NPJ digital medicine · 2022Review
- Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Fluctuations in motor symptoms are mostly observed in Parkinson's disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the "On"/"Off" state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.
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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.