This paper is in the following e-collection/theme issue:

Portable and Mobile Technologies for Rehabilitation (156) Wearable Devices and Sensors (903)

Published on in Vol 5, No 1 (2018): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8335, first published .
A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use

Authors of this article:

Alejandro Rodríguez-Molinero1 Author Orcid Image ;   Carlos Pérez-López2, 3 Author Orcid Image ;   Albert Samà2, 3 Author Orcid Image ;   Eva de Mingo4 Author Orcid Image ;   Daniel Rodríguez-Martín2 Author Orcid Image ;   Jorge Hernández-Vara5 Author Orcid Image ;   Àngels Bayés6 Author Orcid Image ;   Alfons Moral7 Author Orcid Image ;   Ramiro Álvarez8 Author Orcid Image ;   David A Pérez-Martínez9 Author Orcid Image ;   Andreu Català2, 3 Author Orcid Image
Article Authors Cited by (39) Tweetations (2) Metrics

Journals

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  13. Barrachina-Fernández M, Maitín A, Sánchez-Ávila C, Romero J. Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges. Sensors 2021;21(12):4188 View
  14. Milano F, Cerro G, Santoni F, De Angelis A, Miele G, Rodio A, Moschitta A, Ferrigno L, Carbone P. Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements. Sensors 2021;21(12):4196 View
  15. Perrote F, Zeppa G, Coca H, Figueroa S, de Battista J. Evaluación de un sistema de sensores inerciales externos tipo Holter en pacientes con enfermedad de Parkinson en Argentina. Neurología Argentina 2021;13(3):153 View
  16. Sotirakis C, Conway N, Su Z, Villarroel M, Tarassenko L, FitzGerald J, Antoniades C. Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors. Movement Disorders 2022;37(11):2263 View
  17. Thomsen T, Jørgensen L, Kjær T, Haahr A, Vogel A, Larsen I, Winge K. Clinical Markers of 6 Pre-dominant Coping Behaviors in Living With Parkinson Disease: A Convergent Mixed Methods Study. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2022;59 View
  18. Pierleoni P, Raggiunto S, Belli A, Paniccia M, Bazgir O, Palma L. A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study. Applied Sciences 2022;12(11):5486 View
  19. Kenny L, Moore K, O' Riordan C, Fox S, Barton J, Tedesco S, Sica M, Crowe C, Alamäki A, Condell J, Nordström A, Timmons S. The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration. JMIR Formative Research 2022;6(1):e27418 View
  20. Soundararajan R, Prabu A, Routray S, Malla P, Ray A, Palai G, Faragallah O, Baz M, Abualnaja M, Eid M, Rashed A. Deeply Trained Real-Time Body Sensor Networks for Analyzing the Symptoms of Parkinson’s Disease. IEEE Access 2022;10:63403 View
  21. Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo J, Sauco M A, López-Manzanares L, Escalante-Arroyo S, Pérez-Martínez D, Rodríguez-Molinero A. Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial. Frontiers in Neurology 2022;13 View
  22. Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM. Frontiers in Neurology 2022;13 View
  23. Rodríguez-Molinero A, Hernández-Vara J, Miñarro A, Pérez-López C, Bayes-Rusiñol À, Martínez-Castrillo J, Pérez-Martínez D. Multicentre, randomised, single-blind, parallel group trial to compare the effectiveness of a Holter for Parkinson’s symptoms against other clinical monitoring methods: study protocol. BMJ Open 2021;11(7):e045272 View
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  25. de Villers-Sidani É, Voss P, Guitton D, Cisneros-Franco J, Koch N, Ducharme S. A novel tablet-based software for the acquisition and analysis of gaze and eye movement parameters: a preliminary validation study in Parkinson’s disease. Frontiers in Neurology 2023;14 View
  26. Löhle M, Timpka J, Bremer A, Khodakarami H, Gandor F, Horne M, Ebersbach G, Odin P, Storch A. Application of single wrist-wearable accelerometry for objective motor diary assessment in fluctuating Parkinson’s disease. npj Digital Medicine 2023;6(1) View
  27. Sotirakis C, Su Z, Brzezicki M, Conway N, Tarassenko L, FitzGerald J, Antoniades C. Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning. npj Parkinson's Disease 2023;9(1) View
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  31. Sapienza S, Tsurkalenko O, Giraitis M, Mejia A, Zelimkhanov G, Schwaninger I, Klucken J. Assessing the clinical utility of inertial sensors for home monitoring in Parkinson’s disease: a comprehensive review. npj Parkinson's Disease 2024;10(1) View
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  33. Crowe C, Sica M, Kenny L, O’Flynn B, Scott Mueller D, Timmons S, Barton J, Tedesco S. Wearable-Enabled Algorithms for the Estimation of Parkinson’s Symptoms Evaluated in a Continuous Home Monitoring Setting Using Inertial Sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:3828 View

Books/Policy Documents

  1. Harte R, Ó Laighin G, Quinlan L. Digital Health. View
  2. Bianchini E, Maetzler W. Digital Technologies in Movement Disorders. View
  3. Vizcarra J. Handbook of Digital Technologies in Movement Disorders. View
  4. Tönges L, Deuschl G. Handbook of Digital Technologies in Movement Disorders. View