Literature DB >> 33539481

Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.

Marco Sica1, Salvatore Tedesco1, Colum Crowe1, Lorna Kenny2, Kevin Moore2, Suzanne Timmons2, John Barton1, Brendan O'Flynn1, Dimitrios-Sokratis Komaris1.   

Abstract

Parkinson's disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient's condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients' status and the disease's symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson's, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms' assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

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Year:  2021        PMID: 33539481      PMCID: PMC7861548          DOI: 10.1371/journal.pone.0246528

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  65 in total

1.  Center of mass approximation during walking as a function of trunk and swing leg acceleration.

Authors:  A L Betker; T Szturm; Z Moussavi
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

2.  Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer.

Authors:  Aner Weiss; Sarvi Sharifi; Meir Plotnik; Jeroen P P van Vugt; Nir Giladi; Jeffrey M Hausdorff
Journal:  Neurorehabil Neural Repair       Date:  2011 Nov-Dec       Impact factor: 3.919

Review 3.  Motor Complications of Dopaminergic Medications in Parkinson's Disease.

Authors:  Maria Eliza Freitas; Christopher W Hess; Susan H Fox
Journal:  Semin Neurol       Date:  2017-05-16       Impact factor: 3.420

4.  An experience of health technology assessment in new models of care for subjects with Parkinson's disease by means of a new wearable device.

Authors:  Daniele Giansanti; Giovanni Maccioni; Sandra Morelli
Journal:  Telemed J E Health       Date:  2008-06       Impact factor: 3.536

5.  Continuous monitoring of turning in Parkinson's disease: Rehabilitation potential.

Authors:  Martina Mancini; Mahmoud El-Gohary; Sean Pearson; James McNames; Heather Schlueter; John G Nutt; Laurie A King; Fay B Horak
Journal:  NeuroRehabilitation       Date:  2015       Impact factor: 2.138

6.  Accelerometry-based gait analysis and its application to Parkinson's disease assessment--part 1: detection of stride event.

Authors:  Mitsuru Yoneyama; Yosuke Kurihara; Kajiro Watanabe; Hiroshi Mitoma
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-05-02       Impact factor: 3.802

7.  Evaluation of Parkinson's Disease at Home: Predicting Tremor from Wearable Sensors.

Authors:  Margot Heijmans; Jeroen Habets; Mark Kuijf; Pieter Kubben; Christian Herff
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2019-07

8.  Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.

Authors:  Christopher G Goetz; Barbara C Tilley; Stephanie R Shaftman; Glenn T Stebbins; Stanley Fahn; Pablo Martinez-Martin; Werner Poewe; Cristina Sampaio; Matthew B Stern; Richard Dodel; Bruno Dubois; Robert Holloway; Joseph Jankovic; Jaime Kulisevsky; Anthony E Lang; Andrew Lees; Sue Leurgans; Peter A LeWitt; David Nyenhuis; C Warren Olanow; Olivier Rascol; Anette Schrag; Jeanne A Teresi; Jacobus J van Hilten; Nancy LaPelle
Journal:  Mov Disord       Date:  2008-11-15       Impact factor: 10.338

9.  Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Telemed J E Health       Date:  2018-07-03       Impact factor: 3.536

10.  The Hawthorne Effect: a randomised, controlled trial.

Authors:  Rob McCarney; James Warner; Steve Iliffe; Robbert van Haselen; Mark Griffin; Peter Fisher
Journal:  BMC Med Res Methodol       Date:  2007-07-03       Impact factor: 4.615

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  8 in total

1.  Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson's Disease Hand Tremor.

Authors:  Prashanna Khwaounjoo; Gurleen Singh; Sophie Grenfell; Burak Özsoy; Michael R MacAskill; Tim J Anderson; Yusuf O Çakmak
Journal:  Sensors (Basel)       Date:  2022-06-18       Impact factor: 3.847

Review 2.  Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.

Authors:  Konstantina-Maria Giannakopoulou; Ioanna Roussaki; Konstantinos Demestichas
Journal:  Sensors (Basel)       Date:  2022-02-24       Impact factor: 3.576

3.  The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration.

Authors:  Lorna Kenny; Kevin Moore; Clíona O' Riordan; Siobhan Fox; John Barton; Salvatore Tedesco; Marco Sica; Colum Crowe; Antti Alamäki; Joan Condell; Anna Nordström; Suzanne Timmons
Journal:  JMIR Form Res       Date:  2022-01-06

4.  Feasibility of a Multimodal Telemedical Intervention for Patients with Parkinson's Disease-A Pilot Study.

Authors:  Jonas Bendig; Anna-Sophie Wolf; Tony Mark; Anika Frank; Josephine Mathiebe; Madlen Scheibe; Gabriele Müller; Marcus Stahr; Jochen Schmitt; Heinz Reichmann; Kai F Loewenbrück; Björn H Falkenburger
Journal:  J Clin Med       Date:  2022-02-18       Impact factor: 4.241

5.  The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds.

Authors:  Chao Huang; Fuping Zhang; Zhengyi Xu; Jianming Wei
Journal:  Sensors (Basel)       Date:  2022-02-21       Impact factor: 3.576

6.  Unsupervised IMU-based evaluation of at-home exercise programmes: a feasibility study.

Authors:  Dimitrios-Sokratis Komaris; Georgia Tarfali; Brendan O'Flynn; Salvatore Tedesco
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-02-19

7.  Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.

Authors:  Jeroen G V Habets; Christian Herff; Pieter L Kubben; Mark L Kuijf; Yasin Temel; Luc J W Evers; Bastiaan R Bloem; Philip A Starr; Ro'ee Gilron; Simon Little
Journal:  Sensors (Basel)       Date:  2021-11-26       Impact factor: 3.576

8.  Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective.

Authors:  Chao Huang; Fuping Zhang; Zhengyi Xu; Jianming Wei
Journal:  Sensors (Basel)       Date:  2022-04-07       Impact factor: 3.847

  8 in total

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