ArticleNPJ Parkinson's disease2023
Identification of motor progression in Parkinson's disease using wearable sensors and machine learning.
Article in NPJ Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Digital Outcomes as Biomarkers of Disease Progression in Early Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2025Pooled it
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Assessing Changes in Motor Function and Mobility in Individuals with Parkinson's Disease After 12 Sessions of Patient-Specific Adaptive Dynamic Cycling.Sensors (Basel, Switzerland) · 2024Trial
- Lifestyle medicine for neuroprotection: evidence and implementation in Asian contexts.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Proteo-metabolomic integration identifies stage-specific candidate biomarkers for Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Tracking the longitudinal progression of Bradykinesia in Parkinson's disease from videos using VisionMD.Journal of Parkinson's disease · 2026Article
- Enhancing randomized controlled trials through smartwatch-guided participant matching for infectious disease outcomes.Scientific reports · 2026Article
- Wearable-sensor based walking and non-walking measures as progression markers in early to mid-stage Parkinson's disease.NPJ Parkinson's disease · 2026Article
- A Novel Workflow to Estimate Limb Orientation from Wearable Sensors to Monitor Infant Motor Development.Sensors (Basel, Switzerland) · 2026Article
- Wearable sensors for monitoring drug pharmacokinetics in patients with Parkinson's disease.Scientific reports · 2026Article
- Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026Review
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- Relationship between frailty and disease progression in Parkinson's disease: a 3-year longitudinal study.GeroScience · 2026Article
- Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.BMJ open · 2026Article
- WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson's Disease and Age-Matched Controls.Scientific data · 2026Article
- Decreased Physical Activity as an Early Digital Biomarker in Huntington's Disease: A One-Year Observational Study.Brain and behavior · 2026Observational
- The future of clinical trials? Monitoring mobility as an outcome measure in Parkinson's disease: The mobilise-D study.Journal of Parkinson's disease · 2026Article
- Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson's Disease at Risk of Gait Deterioration.Bioengineering (Basel, Switzerland) · 2026Article
- Six-minute walk test instrumented with a single inertial sensor: dataset from healthy adults of different ages.Scientific data · 2026Article
- Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.Frontiers in aging neuroscience · 2026Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.
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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.