Assessment of Insole Based Gait Feature Variation with Progression of Parkinson's Disease

Mazumder, Oishee; Khandelwal, Prateek; Gavas, Rahul; Sinha, Aniruddha

doi:10.1109/icsens.2018.8589635

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…So far, the clinical application of instrumented insoles in PD patients has mostly been limited to the differentiation between PD and controls. Extracted gait and balance parameters have been used successfully for discrimination between the groups (Mazumder et al, 2018;Chatzaki et al, 2021), in line with the findings from established laboratory gait assessments. Furthermore, instrumented insoles have been investigated for their ability to recognize and quantify FoG in PD (Popovic et al, 2010;Shalin et al, 2020;Pardoel et al, 2021).…”

Section: Applicationsmentioning

confidence: 67%

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Salchow-Hömmen

Skrobot

Jochner

et al. 2022

Front. Hum. Neurosci.

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The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

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Section: Applicationsmentioning

confidence: 67%

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Salchow-Hömmen

Skrobot

Jochner

et al. 2022

Front. Hum. Neurosci.

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“…For video recordings for gait analysis, a depth sensor is sometimes used in addition to the 2D image [20]. Force sensors are installed in insoles [21][22][23] and substrates [24].…”

Section: Introductionmentioning

confidence: 99%

“…A standardized test for the diagnosis of Parkinson's disease is the time up and go test. This test is also used, for example, by [22] to differentiate between healthy and Parkinson's patients. We also used this test in [23] under ideal hospital conditions for an approximate estimation of Parkinson's stage.…”

Section: Introductionmentioning

confidence: 99%

Comparison of algorithms and classifiers for stride detection using wearables

Steinmetzer

Bönninger

Reckhardt

et al. 2019

Neural Comput & Applic

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Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min-max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson's disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown. Keywords Stride detection Á Gait analysis Á Inertial sensors Á Parkinson's disease Á Validation Á Dynamic time warping Á Time up and go test Á Convolutional neural networks & Tobias Steinmetzer

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Mediolateral stability index as a biomarker for Parkinson’s disease progression: A graph connectivity based approach

Mazumder

Gavas

Sinha

2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Assessment of Insole Based Gait Feature Variation with Progression of Parkinson's Disease

Cited by 3 publications

References 10 publications

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Comparison of algorithms and classifiers for stride detection using wearables

Mediolateral stability index as a biomarker for Parkinson’s disease progression: A graph connectivity based approach

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