ArticleSensors (Basel, Switzerland)2018
Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Characterizing gait in people with multiple sclerosis using digital data from smartphone sensors: A proposed framework.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Pooled it
- Interpolation and Imputation Strategies for Missing Segments in Continuous Pressure-Flow Cerebral Bio-Signals: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Joint Bayesian Hidden Markov Model With Subject-Specific Transitions for Wearable Sensor Data.Statistics in medicine · 2025Article
- Advancing the Integration of Digital Health Technologies in the Drug Development Ecosystem.Journal of medical Internet research · 2025Article
- Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning in the Parkinson's disease smartwatch (PADS) dataset.NPJ Parkinson's disease · 2024Article
- Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study.JMIR mHealth and uHealth · 2023Article
- Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.NPJ digital medicine · 2022Article
- Remote smartphone monitoring of Parkinson's disease and individual response to therapy.Nature biotechnology · 2022Observational
- Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson's Disease Detection.Diagnostics (Basel, Switzerland) · 2021Article
- Smartphones for Remote Symptom Monitoring of Parkinson's Disease.Journal of Parkinson's disease · 2021Review
- DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data.Sensors (Basel, Switzerland) · 2018Article
- Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability.
Indexed as
Identifiers
What OpenQuestion holds
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.