Detection of gait cycles in treadmill walking using a Kinect

Auvinet, Edouard; Multon, Franck; Aubin, Carl‐Éric; Meunier, Jean; Raison, Maxime

doi:10.1016/j.gaitpost.2014.08.006

Cited by 75 publications

(74 citation statements)

References 5 publications

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“…Stride detection was obtained based on the distance between the knees at a constant estimated knee height [15]. We computed CRP with this Kinect skeleton data on the shank and thigh orientations [12].…”

Section: 31-processing Of Kinect Datamentioning

confidence: 99%

“…Strides were detected using the same method as applied to Kinect data. Vicon and Kinect data were synchronized using longitudinal distance between legs [15]. CRP at shank and thigh were computed using Vicon data [12].…”

Section: 32-processing Of Vicon Datamentioning

confidence: 99%

“…Twelve 4-Megapixel infrared Vicon cameras were placed all around the treadmill and recorded the 3D motion of markers placed over standardised anatomical landmarks at 120Hz [15].…”

Section: 2-protocolmentioning

confidence: 99%

“…Joint centres were computed using the ISB recommendations, and hip joint centre position was computed [20]. The resulting joint positions were filtered with a zero-phase Butterworth filter with a 7Hz cut-off frequency [15] similar to the Kinect data. Strides were detected using the same method as applied to Kinect data.…”

Section: 32-processing Of Vicon Datamentioning

confidence: 99%

“…Accurate determination of global spatiotemporal data [15] and a continuous asymmetry index -the Longitudinal Asymmetry Index (ILong) [16] has been investigated using data obtained from low cost depth cameras such as the Kinect. ILong evaluates gait asymmetry at each instant of the gait cycle according to the instantaneous spatial distance between the right leg motion expressed during a stride starting with a right-foot strike, and the mirrored left leg motion expressed during a stride starting with a left-foot strike.…”

Section: Introductionmentioning

confidence: 99%

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Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data

et al. 2017

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Research highlights:-The Continuous Relative Phase is not useable with Kinect.-New longitudinal index Ilong is sensitive and validated to detect gait asymmetry.-The Ilong index works with Kinect placed either in front of or behind subjects.-Only few strides (at least 5) are needed to compute a valid Ilong. AbstractGait asymmetry information is a key point in disease screening and follow-up. Constant Relative Phase (CRP) has been used to quantify within-stride asymmetry index, which requires noise-free and accurate motion capture, which is difficult to obtain in clinical settings. This study explores a new index, the Longitudinal Asymmetry Index (ILong) which is derived using data from a low-cost depth camera (Kinect). ILong is based on depth images averaged over several gait cycles, rather than derived joint positions or angles. This study aims to evaluate 1) the validity of CRP computed with Kinect, 2) the validity and sensitivity of ILong for measuring gait asymmetry based solely on data provided by a depth camera, 3) the clinical applicability of a posteriorly mounted camera system to avoid occlusion caused by the standard front-fitted treadmill consoles and 4) the number of strides needed to reliably calculate ILong. The gait of 15 subjects was recorded concurrently with a markerbased system (MBS) and Kinect, and asymmetry was artificially reproduced by introducing a 5cm sole attached to one foot. CRP computed with Kinect was not reliable. ILong detected this disturbed gait reliably and could be computed from a posteriorly placed Kinect without loss of validity. A minimum of five strides was needed to achieve a correlation coefficient of 0.9 between standard MBS and low-cost depth camera based ILong. ILong provides a clinically pragmatic method for measuring gait asymmetry, with application for improved patient care through enhanced disease, screening, diagnosis and monitoring.Keywords: Kinect; Gait analysis; gait asymmetry; sensitivity analysis; CRP. Gait asymmetry is typically evaluated using kinematic data and spatio-temporal gait parameters (step/ stride length and duration). Based on these global variables, indicators have been introduced to quantify asymmetry, including the Symmetry Ratio [6] and Symmetry Index [6]. These indicators provide global asymmetry information for a studied gait cycle, but cannot be used to analyse how asymmetry occurs at specific instances within the gait cycle.To overcome this limitation, some authors have proposed analysis of the Constant Relative Phase (CRP) and its derivatives [10]. For a given joint, this indicator constructs a phase diagram based on angular value and angular velocity. Gait asymmetry can be assessed by comparing the CRP of the corresponding left and right joints. It has been used to measure asymmetry caused by weights placed on one foot [11] and pathologies such as stroke [12].However, CRP is based on sagittal joint angles and their derivatives, which impose the use of accurate and noise-free motion capture systems, such as optoelectronic systems [11,12]...

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Section: 31-processing Of Kinect Datamentioning

confidence: 99%

Section: 32-processing Of Vicon Datamentioning

confidence: 99%

“…Twelve 4-Megapixel infrared Vicon cameras were placed all around the treadmill and recorded the 3D motion of markers placed over standardised anatomical landmarks at 120Hz [15].…”

Section: 2-protocolmentioning

confidence: 99%

Section: 32-processing Of Vicon Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data

et al. 2017

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Gait Analysis

Wagner

Mazurek

Morawski

2022

Health Information Science

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Filtered pose graph for efficient kinect pose reconstruction

Plantard

Shum

Multon

2016

Multimed Tools Appl

Self Cite

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Being marker-free and calibration free, Microsoft Kinect is nowadays widely used in many motion-based applications, such as user training for complex industrial tasks and ergonomics pose evaluation. The major problem of Kinect is the placement requirement to obtain accurate poses, as well as its weakness against occlusions. To improve the robustness of Kinect in interactive motion-based applications, real-time data-driven pose reconstruction has been proposed. The idea is to utilize a database of accurately captured human poses as a prior to optimize the Kinect recognized ones, in order to estimate the true poses performed by the user. The key research problem is to identify the most relevant poses in the database for accurate and efficient reconstruction. In this paper, we propose a new pose reconstruction method based on modelling the pose database with a structure called Filtered Pose Graph, which indicates the intrinsic correspondence between poses. Such a graph not only speeds up the database poses selection process, but also improves the relevance of the selected poses for higher quality reconstruction. We apply the proposed method in a challenging environment of industrial context that involves sub-optimal Kinect placement and a large amount of occlusion. Experimental results show that our real-time system reconstructs Kinect poses more accurately than existing methods.

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Detection of gait cycles in treadmill walking using a Kinect

Cited by 75 publications

References 5 publications

Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data

Validity and sensitivity of the longitudinal asymmetry index to detect gait asymmetry using Microsoft Kinect data

Gait Analysis

Filtered pose graph for efficient kinect pose reconstruction

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