Detection of distorted gait and wearing-off phenomenon in Parkinson's disease patients during Levodopa therapy

Moradi, Hamid Reza; Roth, Nils; Seifer, Ann-Kristin; Eskofier, Bjoern M.

doi:10.1109/bhi56158.2022.9926873

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…The gaitmap library itself has been used internally for the past years and was the basis for the data analysis in at least nine papers [44] , [55] , [59] , [60] , [61] , [62] , [63] , [64] and numerous student theses. Furthermore, the developed algorithm pipelines are used by project partners in industry and clinical research.…”

Section: Resultsmentioning

confidence: 99%

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Küderle,

Ullrich,

Roth

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

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Section: Resultsmentioning

confidence: 99%

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Küderle,

Ullrich,

Roth

et al. 2024

IEEE Open J. Eng. Med. Biol.

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“…The gaitmap library itself has been used internally for the past years and was the basis for the data analysis in at least nine papers [45], [56], [60]- [65] and numerous student theses. Furthermore, the developed algorithm pipelines are used…”

Section: B Using and Extending The Ecosystemmentioning

confidence: 99%

Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

Küderle,

Ullrich,

Roth

et al. 2023

Preprint

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<p>Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.</p>

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“…Many of the above solutions, however cutting-edge, are geared toward identifying general neuromuscular implications or symptoms related to PD. However, only a few stateof-the-art solutions [13][14][15][16][17][18] ensure the use of technologies aimed at providing real-time WO detection and objective measurement of the specific phenomenon in PD, which constitute the focus of the present paper.…”

Section: Related Workmentioning

confidence: 99%

“…Two IMUs mounted on the insteps of patients' feet have been also employed by Moradi et al in [17] to detect the WO phenomenon. Each IMU, embedding a 3Daccelerometers and a 3D-gyroscopes, has been used to evaluate three temporal and eight spatial features from each stride on both feet.…”

Section: Related Workmentioning

confidence: 99%

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A Digital Architecture for the Real-Time Tracking of Wearing off Phenomenon in Parkinson’s Disease

Mezzina¹,

Venuto²

2022

Sensors

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Levodopa administration is currently the most common treatment to alleviate Parkinson’s Disease (PD) symptoms. Nevertheless, prolonged use of Levodopa leads to a wearing-off (WO) phenomenon, causing symptoms to reappear. To build a personalized treatment plan aiming to manage PD and its symptoms effectively, there is a need for a technological system able to continuously and objectively assess the WO phenomenon during daily life. In this context, this paper proposes a WO tracker able to exploit neuromuscular data acquired by a dedicated wireless sensor network to discriminate between a Levodopa benefit phase and the reappearance of symptoms. The proposed architecture has been implemented on a heterogeneous computing platform, that statistically analyzes neural and muscular features to identify the best set of features to train the classifier model. Eight models among shallow and deep learning approaches are analyzed in terms of performance, timing and complexity metrics to identify the best inference engine. Experimental results on five subjects experiencing WO, showed that, in the best case, the proposed WO tracker can achieve an accuracy of ~84%, providing the inference in less than 41 ms. It is possible by employing a simple fully-connected neural network with 1 hidden layer and 32 units.

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Detection of distorted gait and wearing-off phenomenon in Parkinson's disease patients during Levodopa therapy

Cited by 6 publications

References 19 publications

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking

Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

A Digital Architecture for the Real-Time Tracking of Wearing off Phenomenon in Parkinson’s Disease

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