ArticleSensors (Basel, Switzerland)2020
A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson's Disease Diagnosis.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- Flexible and Self-Powered Wearable Sensors for Tremor Monitoring in Parkinson'S Disease: Recent Advances in Materials and Device Architectures.Advanced healthcare materials · 2026Review
- Sensorized Motor and Cognitive Dual Task Framework for Dementia Diagnosis: Preliminary Insights From a Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Sensor-based quantification of items used in the MDS-UPDRS-III scale: repetitive lower-limb movements in healthy human participants.Frontiers in neurology · 2025Article
- A Framework for the Human-Centered Design of Service Processes Enabled by Medical Devices: A Case Study of Wearable Devices for Parkinson's Disease.International journal of environmental research and public health · 2024Article
- A Computer Method for Pronation-Supination Assessment in Parkinson's Disease Based on Latent Space Representations of Biomechanical Indicators.Bioengineering (Basel, Switzerland) · 2023Article
- User Profiling to Enhance Clinical Assessment and Human-Robot Interaction: A Feasibility Study.International journal of social robotics · 2023Article
- Mild cognitive impairment identification based on motor and cognitive dual-task pooled indices.PloS one · 2023Article
- Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring.Acta neurologica Scandinavica · 2022Article
- Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes.Sensors (Basel, Switzerland) · 2022Article
- A wearable ring-shaped inertial system to identify action planning impairments during reach-to-grasp sequences: a pilot study.Journal of neuroengineering and rehabilitation · 2021Article
- A System for Neuromotor Based Rehabilitation on a Passive Robotic Aid.Sensors (Basel, Switzerland) · 2021Article
- Innovative motor and cognitive dual-task approaches combining upper and lower limbs may improve dementia early detection.Scientific reports · 2021Article
- A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients.Sensors (Basel, Switzerland) · 2021Article
- Data-Driven Models for Objective Grading Improvement of Parkinson's Disease.Annals of biomedical engineering · 2020Article
- Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Objective assessment of the motor evaluation test for Parkinson's disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.
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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.