original ) (raw )Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score
Kelsey Spear
JAMA neurology, 2018
View PDFchevron_right
HIGH ACCURACY DISCRIMINATION OF PARKINSON'S DESEASE PARTICIPANTS FROM HEALTHY CONTROLS USING SMARTPHONES
Siddharth Arora
View PDFchevron_right
High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones
Sean Donohue
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
View PDFchevron_right
Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild
Vasileios Charisis
Frontiers in ICT
View PDFchevron_right
Towards unobtrusive Parkinson's disease detection via motor symptoms severity inference from multimodal smartphone-sensor data
Zoe Katsarou
2019
View PDFchevron_right
Using AI to measure Parkinson’s disease severity at home
Sangwu Lee
npj Digital Medicine, 2023
View PDFchevron_right
Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones
John Prince
IEEE Transactions on Biomedical Engineering, 2020
View PDFchevron_right
Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes
siddharth arora
Physiological measurement, 2018
View PDFchevron_right
PDkit: an open source data science toolkit for Parkinson's disease
Cosmin Stamate
Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 2019
View PDFchevron_right
Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease
Tapani Keränen
PLOS digital health, 2023
View PDFchevron_right
Editorial: Advancing the treatment landscape in Parkinson's disease using sensor technology and data-driven modeling
Brian Kopell
Frontiers in Aging Neuroscience
View PDFchevron_right
Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
Kevin Biglan
Parkinsonism & related disorders, 2015
View PDFchevron_right
A Multicenter Study Using a Smartwatch, Smartphone, and Wearable Sensors to Assess Early Parkinson’s Disease: Baseline Results of the WATCH-PD Study
stella roberts
Research Square (Research Square), 2022
View PDFchevron_right
Digital outcome measures from smartwatch data relate to non-motor features of Parkinson’s disease
Ann-Kathrin Schalkamp
NPJ Parkinson's disease, 2024
View PDFchevron_right
Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling
Brian Kopell
Frontiers in Computational Neuroscience, 2018
View PDFchevron_right
ParkNosis: Diagnosing Parkinson's disease using mobile phones
Assim Sagahyroon
2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 2016
View PDFchevron_right
Deep learning Parkinson's from smartphone data
Cosmin Stamate
2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2017
View PDFchevron_right
Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study
Cassia Wang
PLOS ONE
View PDFchevron_right
Quantifying Parkinson’s disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
Myrthe Wassenburg
BMJ Open, 2023
View PDFchevron_right
An Algorithmic Approach for Quantitative Evaluation of Parkinson’s Disease Symptoms and Medical Treatment Utilizing Wearables and Multi-Criteria Symptoms Assessment
Mariusz Chmielewski
IEEE Access
View PDFchevron_right
Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning
Chrystalina Antoniades
npj Parkinson's Disease
View PDFchevron_right
The mPower study, Parkinson disease mobile data collected using ResearchKit
Abhishek Pratap
Scientific Data, 2016
View PDFchevron_right
Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers
Ada Zhang
Sensors, 2020
View PDFchevron_right
PDKit: A data science toolkit for the digital assessment of Parkinson’s Disease
Cosmin Stamate
PLoS Computational Biology, 2021
View PDFchevron_right
Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial
Andreas U Monsch
Movement disorders : official journal of the Movement Disorder Society, 2018
View PDFchevron_right
A real-world study of wearable sensors in Parkinson’s disease
Saloni Sharma
npj Parkinson's Disease, 2021
View PDFchevron_right
A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson’s Disease
Ravi Vaidyanathan
IEEE Transactions on Neural Systems and Rehabilitation Engineering
View PDFchevron_right
A smartphone-based system to quantify dexterity in Parkinson's disease patients
Dag Nyholm
Informatics in Medicine Unlocked
View PDFchevron_right
Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods
Luigi Borzi
Electronics, 2022
View PDFchevron_right
Predicting Wearing-Off of Parkinson’s Disease Patients Using a Wrist-Worn Fitness Tracker and a Smartphone: A Case Study
Noel Victorino
Applied Sciences, 2021
View PDFchevron_right