Assessing inertial measurement unit locations for freezing of gait detection and patient preference – ScienceOpen
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      Assessing inertial measurement unit locations for freezing of gait detection and patient preference

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          Abstract

          Background

          Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.

          Methods

          Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.

          Results

          The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively.

          Conclusions

          Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12984-022-00992-x.

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          Most cited references37

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          A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.

          Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
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            Pingouin: statistics in Python

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              Falls and freezing of gait in Parkinson's disease: a review of two interconnected, episodic phenomena.

              Falls and freezing of gait are two "episodic" phenomena that are common in Parkinson's disease. Both symptoms are often incapacitating for affected patients, as the associated physical and psychosocial consequences have a great impact on the patients' quality of life, and survival is diminished. Furthermore, the resultant loss of independence and the treatment costs of injuries add substantially to the health care expenditures associated with Parkinson's disease. In this clinically oriented review, we summarise recent insights into falls and freezing of gait and highlight their similarities, differences, and links. Topics covered include the clinical presentation, recent ideas about the underlying pathophysiology, and the possibilities for treatment. A review of the literature and the current state-of-the-art suggests that clinicians should not feel deterred by the complex nature of falls and freezing of gait; a careful clinical approach may lead to an individually tailored treatment, which can offer at least partial relief for many affected patients. Copyright 2004 Movement Disorder Society
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                Author and article information

                Contributors
                odayj@stanford.edu
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central (London )
                1743-0003
                13 February 2022
                13 February 2022
                2022
                : 19
                : 20
                Affiliations
                [1 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Bioengineering, , Stanford University, ; Stanford, CA USA
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Mechanical Engineering, , Stanford University, ; Stanford, CA USA
                [3 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Neurology and Neurological Sciences, , Stanford University, ; Stanford, CA USA
                [4 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Electrical Engineering, , Stanford University, ; Stanford, CA USA
                [5 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Orthopaedic Surgery, , Stanford University, ; Stanford, CA USA
                [6 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Neurosurgery, , Stanford University, ; Stanford, CA USA
                Author information
                http://orcid.org/0000-0001-7890-5019
                Article
                992
                10.1186/s12984-022-00992-x
                8842967
                35152881
                f4844d53-02cc-4ddd-b41c-b6ef524c36b7
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 September 2021
                : 13 January 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke;
                Award ID: P2CHD101913
                Award ID: UH3 NS107709-01A1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000070, National Institute of Biomedical Imaging and Bioengineering;
                Award ID: P41EB027060
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Funded by: Stanford Bio-X Bowes Graduate Fellowship
                Funded by: Inventec Stanford Graduate Fellowship
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Neurosciences
                freezing of gait,inertial measurement units,machine learning
                Neurosciences
                freezing of gait, inertial measurement units, machine learning

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