An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

Zahradka, Nicole; Verma, Khushboo; Behboodi, Ahad; Bodt, Barry A.; Wright, Henry; Lee, Samuel C. K.

doi:10.3390/s20185272

Cited by 30 publications

(14 citation statements)

References 51 publications

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“…Similarly, for HS prediction, the error values are smaller than for TO prediction as in this study. For instance, Zahradka et al [19] reported a mean error of -10.45ms for a group of healthy and gait-impaired subjects which is very close to our results. Storm et.…”

Section: Discussionsupporting

confidence: 92%

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Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

Aftab

2021

Preprint

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Assessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. Results showed considerable accuracy for the HS with a median error of -1ms. The TO prediction error was larger with the median ranging from 35-84ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects' mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and amputee's functional ability level.

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Section: Discussionsupporting

confidence: 92%

“…A popular choice is to use the shank angular velocity for TO and HS estimation corresponding to the minima in the sagittal-plane angular velocity signal [17]. Many researchers have exploited this signal over the years for diverse subject populations and reported a reasonable degree of accuracy [18, 19, 7, 3, 20, 14, 21, 22, 2].…”

Section: Introductionmentioning

confidence: 99%

Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

Aftab

2021

Preprint

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“…In this study, we chose to include one temporal measure, the stride frequency, which was good to excellent for comfortable gait and stair walking. This confirms earlier studies showing good test-retest reliability for stride time based on inertial sensors [14,31,32].…”

Section: Test-retest Reliabilitysupporting

confidence: 91%

“…Event setting based on shank and foot velocity is more accurate compared to algorithms based on lower trunk movement according to a review [ 19 ]. Zahradka et al analyzed three different algorithms to detect the initial contact in comparison to a gold standard method (force plate): a foot velocity algorithm, a shank velocity algorithm, and a kinematic algorithm based on pelvis, shank, and foot markers [ 32 ]. This study showed that any of the algorithms could be used reliably, but the shank velocity algorithm was suggested as an easy-to-implement method when using inertial sensor systems.…”

Section: Discussionmentioning

confidence: 99%

Test-Retest Reliability of Kinematic and Temporal Outcome Measures for Clinical Gait and Stair Walking Tests, Based on Wearable Inertial Sensors

Nilsson

Ertzgaard

Lundgren

et al. 2022

Sensors

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It is important to assess gait function in neurological disorders. A common outcome measure from clinical walking tests is average speed, which is reliable but does not capture important kinematical and temporal aspects of gait function. An extended gait analysis must be time efficient and reliable to be included in the clinical routine. The aim of this study was to add an inertial sensor system to a gait test battery and analyze the test-retest reliability of kinematic and temporal outcome measures. Measurements and analyses were performed in the hospital environment by physiotherapists using customized software. In total, 22 healthy persons performed comfortable gait, fast gait, and stair walking, with 12 inertial sensors attached to the feet, shank, thigh, pelvis, thorax, and arms. Each person participated in 2 test sessions, with about 3–6 days between the sessions. Kinematics were calculated based on a sensor fusion algorithm. Sagittal peak angles, sagittal range of motion, and stride frequency were derived. Intraclass-correlation coefficients were determined to analyze the test-retest reliability, which was good to excellent for comfortable and fast gait, with exceptions for hip, knee, and ankle peak angles during fast gait, which showed moderate reliability, and fast gait stride frequency, which showed poor reliability. In stair walking, all outcome measures except shoulder extension showed good to excellent reliability. Inertial sensors have the potential to improve the clinical evaluation of gait function in neurological patients, but this must be verified in patient groups.

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“…Overall, more detailed segmentation of the gait cycle and calculation of movement parameters that provide a relationship to typical recovery patterns in clinical populations allow clinicians to make more objective and detailed assessments of the deviation in pathological gait and the improvements in rehabilitation progress. Furthermore, gait event detection is also critical for applications in which gait events are served to trigger assistive devices, as well as when considering the use of orthotic or therapeutic interventions, particularly in functional electrical stimulation [68].…”

Section: Discussionmentioning

confidence: 99%

Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

Wu¹,

Maurenbrecher²,

Schaer³

et al. 2021

Preprint

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<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

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An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based ‘Gold Standard’

Cited by 30 publications

References 51 publications

Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

Test-Retest Reliability of Kinematic and Temporal Outcome Measures for Clinical Gait and Stair Walking Tests, Based on Wearable Inertial Sensors

Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

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