Using horizontal heel displacement to identify heel strike instants in normal gait

Banks, Jacob J.; Chang, Wen-Ruey; Xu, Xu; Chang, Chien-Chi

doi:10.1016/j.gaitpost.2015.03.015

Cited by 28 publications

(15 citation statements)

References 13 publications

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“…The algorithm was validated in Zeni et al [40]. Therefore, it was considered a valid approach for the detection of IC and TC and was employed in this study rather than an alternative approach [43].…”

Section: Discussionmentioning

confidence: 99%

Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters

Teufl

Lorenz

Miezal

et al. 2018

Sensors

101

102

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The aim of this study was to assess the validity and test-retest reliability of an inertial measurement unit (IMU) system for gait analysis. Twenty-four healthy subjects conducted a 6-min walking test and were instrumented with seven IMUs and retroreflective markers. A kinematic approach was used to estimate the initial and terminal contact events in real-time. Based on these events twelve spatio-temporal parameters (STP) were calculated. A marker based optical motion capture (OMC) system provided the reference. Event-detection rate was about 99%. Detection offset was below 0.017 s. Relative root mean square error (RMSE) ranged from 0.90% to 4.40% for most parameters. However, the parameters that require spatial information of both feet showed higher errors. Step length showed a relative RMSE of 6.69%. Step width and swing width revealed the highest relative RMSE (34.34% and 35.20%). Test-retest results ranged from 0.67 to 0.92, except for the step width (0.25). Summarizing, it appears that the parameters describing the lateral distance between the feet need further improvement. However, the results of the validity and reliability of the IMU system encourage its validation in clinical settings as well as further research.

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Section: Discussionmentioning

confidence: 99%

Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters

Teufl

Lorenz

Miezal

et al. 2018

Sensors

101

102

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show abstract

“…Although this is a simple procedure, it does not always yield similar results to those using actual gait events across all gait speeds and subjects when detecting toe-off. In order to achieve better accuracy, the trajectory of the horizontal ankle position (x-coordinate of the ankle) in relation to the hip was determined using a forward kinematic model to detect gait events [17, 25, 26] and the results were compared to the data obtained using a force plate treadmill (Supplementary Materials). Similar to other studies that have used 3-D camera systems [17, 25, 26], we found that the horizontal ankle position during gait could serve as an accurate surrogate method to define stance and swing phases of gait cycles (Supplementary Table 5).…”

Section: Discussionmentioning

confidence: 99%

Reliable sagittal plane kinematic gait assessments are feasible using low-cost webcam technology

2017

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Three-dimensional (3-D) motion capture systems are commonly used for gait analysis because they provide reliable and accurate measurements. However, the downside of this approach is that it is expensive and requires technical expertise; thus making it less feasible in the clinic. To address this limitation, we recently developed and validated (using a high-precision walking robot) a low-cost, two-dimensional (2-D) real-time motion tracking approach using a simple webcam and LabVIEW Vision Assistant. The purpose of this study was to establish the repeatability and minimal detectable change values of hip and knee sagittal plane gait kinematics recorded using this system. Twenty-one healthy subjects underwent two kinematic assessments while walking on a treadmill at a range of gait velocities. Intraclass correlation coefficients (ICC) and minimal detectable change (MDC) values were calculated for commonly used hip and knee kinematic parameters to demonstrate the reliability of the system. Additionally, Bland-Altman plots were generated to examine the agreement between the measurements recorded on two different days. The system demonstrated good to excellent reliability (ICC > 0.75) for all the gait parameters tested on this study. The MDC values were typically low (< 5°) for most of the parameters. The Bland-Altman plots indicated that there was no systematic error or bias in kinematic measurements and showed good agreement between measurements obtained on two different days. These results indicate that kinematic gait assessments using webcam technology can be reliably used for clinical and research purposes.

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“…For comparison, the features were also calculated based on the OMC data, also segmented to 100% GC. In this case the IC was estimated based on the markers placed on the heel, toe and the pelvis and a custom written Matlab (Mathworks Inc., Natick, MA, USA) script incorporating an approach based on [50,51]. Here, feature Set 1 and 2 counted 1937 samples per feature (1404 labeled “healthy”, aka Control Group; 533 labeled “patient”, aka THA Group).…”

Section: Methodsmentioning

confidence: 99%

Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features

Teufl

Taetz

Miezal

et al. 2019

Sensors

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Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.

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Using horizontal heel displacement to identify heel strike instants in normal gait

Cited by 28 publications

References 13 publications

Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters

Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters

Reliable sagittal plane kinematic gait assessments are feasible using low-cost webcam technology

Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features

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