Filtered pose graph for efficient kinect pose reconstruction

Plantard, Pierre; Shum, Hubert P. H.; Multon, Franck

doi:10.1007/s11042-016-3546-4

Cited by 31 publications

(18 citation statements)

References 40 publications

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“…It would also be interesting to evaluate the sensitivity of the method when tuning this interval in each subject' ca e. Moreover, we could also explore how to deal with the total body for patients with impaired gaits that could be associated with an asymmetric posture of the upper-body. The present protocol has some limitations such as 1) the small number of subjects who participated in the study, 2) only one gait perturbation was tested using a sole below one foot, and 3) recent Kinect correction methods [23] could be used which may improve the CRP. Despite these shortcomings, determining ILong can be automated.…”

Section: -Discussionmentioning

confidence: 99%

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: -Discussionmentioning

confidence: 99%

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

et al. 2017

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“…Uncorrected Kinect data exhibited much higher errors than corrected ones, which may lead to difficulties when using the system in real working environments. However, the correction method (Plantard et al, 2017a) shows a practical capacity to correctly provide ergonomic evaluation for working tasks with a cheap and easy-to-use system. Figure 6 depicts an example of potential application based on our framework, where joint angles and resulting RULA score are given to the ergonomists.…”

Section: Discussionmentioning

confidence: 99%

Usability of corrected Kinect measurement for ergonomic evaluation in constrained environment

Plantard¹,

Shum²,

Multon³

2017

IJHFMS

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Evaluation of potential risks of musculoskeletal disorders in real workstations is challenging as the environment is cluttered, which makes it difficult to correctly assess the pose of a worker. Being marker-free and calibration-free, Microsoft Kinect is a promising device to assess these poses, but it can deliver unreliable poses especially when occlusions occur. To overcome this problem, we propose to detect and correct badly recognised body parts thanks to a database of example poses. We applied the proposed method to compute rapid upper limb assessment (RULA) score in a realistic environment that involved sub-optimal Kinect placement and several types of occlusions. Results showed that when occlusions occur, the inaccurate raw Kinect data could be significantly improved using our correction method, leading to acceptable joint angles and RULA scores. Our method opens new perspectives to define new fatigue or solicitation indexes based on continuous measurement contrary to classical static images used in ergonomics.

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“…In this section, we give an overview of the correction framework presented in Plantard et al (2017a). The readers are referred to Plantard et al (2017a) for more details.…”

Section: Correction Frameworkmentioning

confidence: 99%

“…The readers are referred to Plantard et al (2017a) for more details. The correction method improves the interpretation of Kinect data thanks to an example-based approach.…”

Section: Correction Frameworkmentioning

confidence: 99%

“…Recently, an example-based correction approach has been proposed to deal with situations where when occlusions occur (Plantard et al, 2017a). A new data structure named filtered pose graph guarantees efficient preselection of a relevant subset of poses before correction, ensuring continuity and maximising reliability even when important occlusions occur.…”

Section: Introductionmentioning

confidence: 99%

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Usability of Corrected Kinect Measurement for Ergonomic Evaluation in Constrained Environment

Multon

Shum

Plantard

2017

IJHFMS

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Filtered pose graph for efficient kinect pose reconstruction

Cited by 31 publications

References 40 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

Usability of corrected Kinect measurement for ergonomic evaluation in constrained environment

Usability of Corrected Kinect Measurement for Ergonomic Evaluation in Constrained Environment

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