Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables

Clark, Ross; Bower, Kelly J; Mentiplay, Benjamin F.; Paterson, Kade L.; Pua, Yong-Hao

doi:10.1016/j.jbiomech.2013.08.011

Cited by 208 publications

(210 citation statements)

References 17 publications

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“…This pilot study is consistent with previous literature comparing the Kinect and a three-dimensional goldstandard alternative in the assessment of postural control and gait [30][31]51]. Along with the present study, previ ous research has suggested varied accuracy across out comes and unreliable body segment lengths affecting accuracy of joint positioning.…”

Section: Future Worksupporting

confidence: 92%

“…While the Kinect was origi nally designed for gaming, it is currently being used as a rehabilitation tool for intervention in stroke [26], cerebral palsy [27], cognitive dysfunction [28], and fall preven tion [29]. The Kinect is also being used for the assess ment of postural control [30], gait [31], balance [3], static foot posture [32], body sway [33], and common motor movements [34], all with promising comparisons to three-dimensional gold standard systems. The use of the Kinect as a rehabilitation tool outside of the clinic and in the home [35][36][37] suggests the sensor's portability and supports the recommendation of previous research to incorporate assessment throughout the continuum of care [38].…”

Section: Microsoft Kinectmentioning

confidence: 99%

“…The Microsoft Kinect sensor is a portable, inexpen sive motion-sensing device that can be used in conjunc tion with a personal computer to monitor the threedimensional position and orientation of a person's body segments without the use of reflective markers or wear able devices [31]. During data collection, one laptop com puter and one Kinect sensor were placed in front and to the right of the participant on top of a wooden stand 1.5 m high, angled toward the center of the participant's right wheel axle to measure the movement of the head; neck; torso; and right shoulder, elbow, wrist, and hand at a sam pling rate of approximately 30 Hz.…”

Section: Microsoft Kinectmentioning

confidence: 99%

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Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Milgrom¹,

OTD²,

Foreman³

et al. 2016

J Rehabil Res Dev

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Abstract-Concurrent validity and test-retest reliability of the Microsoft Kinect in quantification of manual wheelchair pro pulsion were examined. Data were collected from five manual wheelchair users on a roller system. Three Kinect sensors were used to assess test-retest reliability with a still pose. Three sys tems were used to assess concurrent validity of the Kinect to measure propulsion kinematics (joint angles, push loop charac teristics): Kinect, Motion Analysis, and Dartfish ProSuite (Dart fish joint angles were limited to shoulder and elbow flexion). Intraclass correlation coefficients (ICCs) revealed good reliabil ity (0.87-0.99) between five of the six joint angles (neck flexion, shoulder flexion, shoulder abduction, elbow flexion, wrist flexion). ICCs suggested good concurrent validity for elbow flexion between the Kinect and Dartfish and between the Kinect and Motion Analysis. Good concurrent validity was revealed for maximum height, hand-axle relationship, and maximum area (0.92-0.95) between the Kinect and Dartfish and maxi mum height and hand-axle relationship (0.89-0.96) between the Kinect and Motion Analysis. Analysis of variance revealed sig nificant differences (p < 0.05) in maximum length between Dartfish (mean 58.76 cm) and the Kinect (40.16 cm). Results pose promising research and clinical implications for propul sion assessment and overuse injury prevention with the applica tion of current findings to future technology.

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Section: Future Worksupporting

confidence: 92%

Section: Microsoft Kinectmentioning

confidence: 99%

Section: Microsoft Kinectmentioning

confidence: 99%

See 1 more Smart Citation

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Milgrom¹,

OTD²,

Foreman³

et al. 2016

J Rehabil Res Dev

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“…Previous works reported good accuracy of the Kinect when analysing simple full body motion such as gait [11], or reaching movements [10]. It has also shown high accuracy in terms of timing and range during large movements, with potential application in clinical assessment of Parkinson's diseases [16] and in rehabilitation [14].…”

Section: Depth Camera Based Motion Analysismentioning

confidence: 99%

Filtered pose graph for efficient kinect pose reconstruction

Plantard

Shum

Multon

2016

Multimed Tools Appl

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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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“…Such systems are difficult to use in daily practice in small clinics as they require specific technical skills, calibration, and manual processing. In everyday practice, especially in small clinics, easy-to-use and low-cost systems are preferable, such as depth cameras developed for the gaming industries, which have been recently proposed for clinical gait analysis [13,14]. However, the authors reported problems when computing temporal information due to inaccuracies and noise.…”

Section: Introductionmentioning

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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Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables

Cited by 208 publications

References 17 publications

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Filtered pose graph for efficient kinect pose reconstruction

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

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