ArticleSensors (Basel, Switzerland)2018
A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson's Disease.
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.
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.
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease.Aging clinical and experimental research · 2023Article
- Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms.Sensors (Basel, Switzerland) · 2022Article
- Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect.Sensors (Basel, Switzerland) · 2022Article
- Self-management Interventions for People With Parkinson Disease: Scoping Review.Journal of medical Internet research · 2022Article
- Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.Journal of neuroengineering and rehabilitation · 2021Article
- Monitoring of Gait Parameters in Post-Stroke Individuals: A Feasibility Study Using RGB-D Sensors.Sensors (Basel, Switzerland) · 2021Article
- An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson's Disease.Sensors (Basel, Switzerland) · 2019Article
- An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2019Article
- Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson's Disease.Sensors (Basel, Switzerland) · 2019Article
- Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson's Disease Using Electromyography and Inertial Signals.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson's Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD.
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.