An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Wu, Jiaen; Kuruvithadam, Kiran; Schaer, Alessandro; Stoneham, R. C.; Chatzipirpiridis, George; Easthope, Chris; Barry, Gill; Martin, J. M.; Nelson, Bradley J.; Ergeneman, Olgaç; Torun, Hamdi

doi:10.3390/s21082869

Cited by 19 publications

(17 citation statements)

References 43 publications

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“…Wearable inertial measurement units (IMUs) address some of these challenges inherent to 3D motion capture and also offer potential for cost-effective portable clinical gait analysis (Wu et al, 2021 ; McDevitt et al, 2022 ). Wearable devices such as the APDM Opal IMU-based system have been promoted for use in clinical populations to monitor gait and mobility in individuals with neurodegenerative conditions such as Parkinson’s disease and have recently been advanced as digital biomarker endpoints for clinical trials (Mancini et al, 2015 ; Mancini and Horak, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Riazati

McGuirk²,

Perry³

et al. 2022

Front. Hum. Neurosci.

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Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings.Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%.Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%.Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

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Section: Introductionmentioning

confidence: 99%

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Riazati

McGuirk²,

Perry³

et al. 2022

Front. Hum. Neurosci.

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“…All walking data were collected by the wearable gait analysis system Nushu developed by Magnes AG [33]. It consists of a pair of shoes and a mobile (iOS) application.…”

Section: Data Collection Systemmentioning

confidence: 99%

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

Becsek²,

Schaer³

et al. 2022

Preprint

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Detecting gait phases unobtrusively and reliably in real-time for long-term unsupervised walking is important for clinical gait rehabilitation and early diagnosis of neurological diseases. Due to hardware limitations in wearable devices, real-time gait phase detection remains a challenge for unsupervised mobility assessment. In this work, a hybrid algorithm combining a reduced support vector machine (RSVM) and a finite state machine (FSM) is developed to address this. The RSVM is developed by reducing the SVM model size for real-time applications through a novel technique based on K-means clustering, and the FSM validates predictions and corrects misclassifications of RSVM. With the proposed algorithm, the model size is reduced by 88%, and the computation time is reduced by a factor of 36, with only a minor degradation in performance. The real-time classification performance of the algorithm is evaluated by twenty healthy subjects walking outdoors with unsupervised gait. The proposed algorithm demonstrated promising real-time performance, with the accuracy, sensitivity, and specificity of 91.51%, 91.70%, 95.77% across all test subjects.

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“…All walking data were collected by the wearable gait analysis system Nushu developed by Magnes AG [21]. It consists of a pair of shoes and a mobile (iOS) application.…”

Section: B Data Collection Systemmentioning

confidence: 99%

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

Becsek²,

Schaer³

et al. 2022

Preprint

Self Cite

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Detecting gait phases unobtrusively and reliablyin real-time for long-term unsupervised walking isimportant for clinical gait rehabilitation and early diagnosisof neurological diseases. Due to hardware limitations inwearable devices (e.g., memory and computation power),reliable real-time gait phase detection remains a challengefor unsupervised mobility assessment. In this work, a hybridalgorithm combining a reduced support vector machine(RSVM) and a finite state machine (FSM) is developedto address this. K-means clustering is used to reduce thenumber of support vectors (SVs) by constructing a smallerdataset that contains the most informative data points.For each gait phase prediction, an FSM is designed tovalidate the prediction and correct misclassifications. AfterSV reduction, the model size is reduced by 88%, and thecomputation time is reduced by a factor of 36, with onlya minor degradation in prediction performance of 4.12%,2.34%, and 4.85% for sensitivity, specificity, and accuracy,respectively. The real-time classification performance of thealgorithm is evaluated by twenty healthy subjects walkingalong a predefined route with unsupervised free-living gait.The proposed algorithm demonstrated promising real-timeperformance, with an accuracy of 91.51%, a sensitivity of91.70%, and a specificity of 95.77% across all test subjects.The algorithm also demonstrated its robustness with respectto different values of walking speed, cadence, andstride length.

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An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Cited by 19 publications

References 43 publications

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

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