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

Portable and Mobile Technologies for Rehabilitation (151) Wearable Devices and Sensors (888)

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 (38) Tweetations (2) Metrics

Journals

  1. Thorp J, Adamczyk P, Ploeg H, Pickett K. Monitoring Motor Symptoms During Activities of Daily Living in Individuals With Parkinson's Disease. Frontiers in Neurology 2018;9 View
  2. Teshuva I, Hillel I, Gazit E, Giladi N, Mirelman A, Hausdorff J. Using wearables to assess bradykinesia and rigidity in patients with Parkinson’s disease: a focused, narrative review of the literature. Journal of Neural Transmission 2019;126(6):699 View
  3. Habets J, Heijmans M, Herff C, Simons C, Leentjens A, Temel Y, Kuijf M, Kubben P. Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments. JMIR mHealth and uHealth 2020;8(5):e15628 View
  4. Evers L, Raykov Y, Krijthe J, Silva de Lima A, Badawy R, Claes K, Heskes T, Little M, Meinders M, Bloem B. Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study. Journal of Medical Internet Research 2020;22(10):e19068 View
  5. Rodríguez-Molinero A, Pérez-López C, Samà A, Rodríguez-Martín D, Alcaine S, Mestre B, Quispe P, Giuliani B, Vainstein G, Browne P, Sweeney D, Quinlan L, Arostegui J, Bayes À, Lewy H, Costa A, Annicchiarico R, Counihan T, Laighin G, Cabestany J. Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study. Scientific Reports 2019;9(1) View
  6. Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone A. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson’s Disease in the Home or a Home-like Environment. Journal of Parkinson's Disease 2020;10(2):429 View
  7. Hørmann Thomsen T, Kjær T, Bastrup Jørgensen L, Haahr A, Winge K. “Does the Response to Morning Medication Predict the ADL-Level of the Day in Parkinson’s Disease?”. Parkinson's Disease 2020;2020:1 View
  8. Gurchiek R, Choquette R, Beynnon B, Slauterbeck J, Tourville T, Toth M, McGinnis R. Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application. Scientific Reports 2019;9(1) View
  9. Zesiewicz T. Parkinson Disease. CONTINUUM: Lifelong Learning in Neurology 2019;25(4):896 View
  10. Del Din S, Kirk C, Yarnall A, Rochester L, Hausdorff J, Mirelman A, Dorsey E, Brundin P, Bloem B. Body-Worn Sensors for Remote Monitoring of Parkinson’s Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead. Journal of Parkinson's Disease 2021;11(s1):S35 View
  11. Habets J, Heijmans M, Leentjens A, Simons C, Temel Y, Kuijf M, Kubben P, Herff C. A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. Data 2021;6(2):22 View
  12. Heidarivincheh F, McConville R, Morgan C, McNaney R, Masullo A, Mirmehdi M, Whone A, Craddock I. Multimodal Classification of Parkinson’s Disease in Home Environments with Resiliency to Missing Modalities. Sensors 2021;21(12):4133 View
  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
  24. Guerra A, D’Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson’s disease. Expert Review of Neurotherapeutics 2023;23(8):689 View
  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
  28. Fietzek U, Messner M, Levin J. Kann KI Parkinson?. Nervenheilkunde 2023;42(09):612 View
  29. Feldmann L, Roudini J, Kühn A, Habets J. Improving naturalistic neuroscience with patient engagement strategies. Frontiers in Human Neuroscience 2024;17 View
  30. Cox E, Wade R, Hodgson R, Fulbright H, Phung T, Meader N, Walker S, Rothery C, Simmonds M. Devices for remote continuous monitoring of people with Parkinson’s disease: a systematic review and cost-effectiveness analysis. Health Technology Assessment 2024:1 View
  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
  32. Shuqair M, Jimenez-Shahed J, Ghoraani B. Reinforcement Learning-Based Adaptive Classification for Medication State Monitoring in Parkinson's Disease. IEEE Journal of Biomedical and Health Informatics 2024;28(10):6168 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